This document summarizes an analysis of Medicare Part D opioid prescription data conducted to identify potentially fraudulent prescribers. The analysis used multivariate statistical techniques like calculating Mahalanobis distance to identify outliers after accounting for correlations between variables like total opioid supply and number of patients. Several prescribers identified as outliers were validated as criminal cases, showing the potential of this approach. The document concludes the data and methods can help narrow investigations but notes limitations like data quality, timeliness and lack of dependent fraud variables that need addressing for optimal results.
Lantern Pharma leverages AI, machine learning and genomics to rescue and develop targeted cancer therapies. The company's RADR platform contains over 4.6 billion data points covering drug and tumor interactions. Lantern is developing a portfolio of targeted cancer drugs including LP-100, LP-184, LP-300 and novel ADC programs. The company aims to identify patient populations most likely to respond to therapies to streamline drug development and maximize success.
Cidara is developing long-acting therapeutics designed to improve the standard of care for patients facing serious diseases. The Company’s portfolio is comprised of drug candidates intended to transform existing treatment and prevention paradigms. Its lead Phase 3 antifungal candidate, rezafungin, will report Phase 3 data at the end of 2021. The potential peak sales opportunity for rezafungin in the US is ~$750M. In addition, the Company is developing Drug-Fc Conjugates (DFCs) targeting viral and oncology diseases from Cidara’s proprietary Cloudbreak® platform.
Kiromic BioPharma, Inc. is a target discovery and gene-editing company utilizing artificial intelligence and its proprietary neural network platform with a therapeutic focus on immuno-oncology.
Reviva Pharmaceuticals Holdings Inc is a clinical development pharmaceutical company. It is developing a portfolio of internally discovered therapies that address unmet medical needs in the areas of central nervous system, cardiovascular, metabolic and inflammatory diseases.
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
Marijuana, Opioids and State Laws – What HR Teams Need to KnowCareerBuilder
This document summarizes a presentation on workplace drug testing and compliance issues. It discusses evolving issues in the drug testing industry, including medical marijuana and prescription drug laws. It provides an overview of federal laws and how they differ from varying state laws on these topics. Key court cases are mentioned, and employers' obligations under the ADA regarding prescription drug testing are reviewed. Challenges for employers around the opioid epidemic are also examined. State-specific compliance rules for drug testing programs are outlined.
Cidara is developing long-acting therapeutics designed to improve the standard of care for patients facing serious diseases. The Company’s portfolio is comprised of drug candidates intended to transform existing treatment and prevention paradigms. Its lead Phase 3 antifungal candidate, rezafungin, will report Phase 3 data at the end of 2021. The potential peak sales opportunity for rezafungin in the US is ~$750M. In addition, the Company is developing Drug-Fc Conjugates (DFCs) targeting viral and oncology diseases from Cidara’s proprietary Cloudbreak® platform.
This document summarizes an analysis of Medicare Part D opioid prescription data conducted to identify potentially fraudulent prescribers. The analysis used multivariate statistical techniques like calculating Mahalanobis distance to identify outliers after accounting for correlations between variables like total opioid supply and number of patients. Several prescribers identified as outliers were validated as criminal cases, showing the potential of this approach. The document concludes the data and methods can help narrow investigations but notes limitations like data quality, timeliness and lack of dependent fraud variables that need addressing for optimal results.
Lantern Pharma leverages AI, machine learning and genomics to rescue and develop targeted cancer therapies. The company's RADR platform contains over 4.6 billion data points covering drug and tumor interactions. Lantern is developing a portfolio of targeted cancer drugs including LP-100, LP-184, LP-300 and novel ADC programs. The company aims to identify patient populations most likely to respond to therapies to streamline drug development and maximize success.
Cidara is developing long-acting therapeutics designed to improve the standard of care for patients facing serious diseases. The Company’s portfolio is comprised of drug candidates intended to transform existing treatment and prevention paradigms. Its lead Phase 3 antifungal candidate, rezafungin, will report Phase 3 data at the end of 2021. The potential peak sales opportunity for rezafungin in the US is ~$750M. In addition, the Company is developing Drug-Fc Conjugates (DFCs) targeting viral and oncology diseases from Cidara’s proprietary Cloudbreak® platform.
Kiromic BioPharma, Inc. is a target discovery and gene-editing company utilizing artificial intelligence and its proprietary neural network platform with a therapeutic focus on immuno-oncology.
Reviva Pharmaceuticals Holdings Inc is a clinical development pharmaceutical company. It is developing a portfolio of internally discovered therapies that address unmet medical needs in the areas of central nervous system, cardiovascular, metabolic and inflammatory diseases.
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
Marijuana, Opioids and State Laws – What HR Teams Need to KnowCareerBuilder
This document summarizes a presentation on workplace drug testing and compliance issues. It discusses evolving issues in the drug testing industry, including medical marijuana and prescription drug laws. It provides an overview of federal laws and how they differ from varying state laws on these topics. Key court cases are mentioned, and employers' obligations under the ADA regarding prescription drug testing are reviewed. Challenges for employers around the opioid epidemic are also examined. State-specific compliance rules for drug testing programs are outlined.
Cidara is developing long-acting therapeutics designed to improve the standard of care for patients facing serious diseases. The Company’s portfolio is comprised of drug candidates intended to transform existing treatment and prevention paradigms. Its lead Phase 3 antifungal candidate, rezafungin, will report Phase 3 data at the end of 2021. The potential peak sales opportunity for rezafungin in the US is ~$750M. In addition, the Company is developing Drug-Fc Conjugates (DFCs) targeting viral and oncology diseases from Cidara’s proprietary Cloudbreak® platform.
NE3107 is a small molecule in Phase 3 clinical trials for Alzheimer's disease and Parkinson's disease. It works by inhibiting neuroinflammation and insulin resistance, two key drivers of cognitive decline. A Phase 3 trial for Alzheimer's is underway testing NE3107's ability to slow cognitive decline compared to placebo. Preclinical studies show NE3107 reduces inflammation, enhances insulin sensitivity, and has neuroprotective effects, supporting its potential in neurodegenerative diseases. A Phase 2 trial will assess NE3107's activity and safety when combined with L-dopa for Parkinson's disease.
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
Lantern Pharma leverages AI, machine learning and genomic data to transform oncology drug development. Their RADR platform identifies patient subgroups likely to respond to drug candidates to increase success rates. They are developing multiple oncology drug programs including LP-100 for prostate cancer through a precision trial. LP-300 targets never-smoker NSCLC adenocarcinoma based on prior data and biomarker studies. Lantern aims to reduce costs and time through AI-enabled approaches.
One in 8 U.S. women will develop invasive breast cancer over her lifetime, with approximately 266,000 new breast cancer patients and 3.1 million breast cancer survivors in 2018. Following breast cancer surgery in the adjuvant setting, a HER2/neu 3+ patient typically receives Herceptin® in the first year, with the hope that their breast cancer will not recur, and with the odds of recurrence slowly decreasing over the first 5 years after surgery. Herceptin® has been shown to reduce recurrence rates from 25% to 12% in the adjuvant setting. In the neoadjuvant setting, a patient receives treatment before surgery and based on the results of a biopsy at surgery, will receive the same or more potent treatment after surgery. Kadcyla® has been shown to reduce recurrence rates from 22% to 11% in the neoadjuvant setting. Accordingly, we believe that GP2 may be used to address the 50% of recurring patients who do not respond to either Herceptin® or Kadcyla®.
This report examines CMS's oversight of Medicare Part D beneficiaries who receive opioid prescriptions and providers who prescribe opioids to these beneficiaries. It finds that while CMS provides guidance to Part D plan sponsors on monitoring beneficiaries at high risk of opioid overuse, it lacks complete data on the full population of beneficiaries at risk. It also finds that CMS oversees prescribing through its contractor NBI MEDIC but does not specifically analyze opioid prescription data or require reporting on actions taken regarding inappropriate opioid prescribing. The report concludes that CMS needs more comprehensive oversight to reduce the risks of opioid misuse, overdose, and inappropriate prescribing among Medicare beneficiaries.
This document provides an overview of drug courts in the United States, including their background and effectiveness. It discusses how drug courts work, their growth across the country since 1989, and their goal of reducing drug use and recidivism among nonviolent offenders through intensive supervision and court-mandated drug treatment. It also reviews studies that have found drug courts can reduce recidivism and describes the federal grant program that has helped fund drug courts since 1994, allocating over $530 million. Issues like how to measure effectiveness and whether more serious offenders could be included are discussed.
Virios Therapeutics is a clinical-stage biotechnology company focused on advancing novel, dual mechanism antiviral therapies to treat conditions associated with virally triggered or maintained immune responses, such as Fibromyalgia (“FM”). Immune responses related to the activation of tissue resident Herpes Simplex Virus-1 (“HSV-1”) have been postulated as a potential root cause triggering and/or sustaining chronic illnesses such as FM, irritable bowel disease (“IBS”), and chronic fatigue syndrome, all of which can be characterized by waxing and waning symptom flare-ups with no obvious etiology. Virios’ lead development candidate (“IMC-1”) is a novel, proprietary, fixed dose combination of famciclovir and celecoxib designed to synergistically suppress HSV-1 replication, with the end goal of reducing virally promoted disease symptoms.
This document summarizes a presentation on using prescription drug data to limit misuse and abuse by third-party payers. The presentation features speakers from myMatrixx and Express Scripts discussing how data from sources like prescription drug monitoring programs, the DEA, NPPES, proprietary databases, and pharmaceutical manufacturers can be mined and analyzed to identify problematic prescribing patterns, problem geographic areas, and individual doctors who may be recklessly prescribing controlled substances. The goal is to organize collaboration between private and public agencies to help address the epidemic of prescription drug abuse.
Reviva Pharmaceuticals Holdings Inc is a clinical development pharmaceutical company. It is developing a portfolio of internally discovered therapies that address unmet medical needs in the areas of central nervous system, cardiovascular, metabolic and inflammatory diseases.
Reviva Pharmaceuticals Holdings Inc is a clinical development pharmaceutical company. It is developing a portfolio of internally discovered therapies that address unmet medical needs in the areas of central nervous system, cardiovascular, metabolic and inflammatory diseases.
This document summarizes a report on cardiac toxicity. Cardiac toxicity, where drugs cause heart damage, has led to 28% of drug withdrawals over the last 30 years in the US. If cardiac toxicity is found during drug development or after launch, it can terminate development programs or require drugs to be withdrawn from the market. The report covers the physiology, pharmacology, clinical testing, regulation, and commercial implications of cardiac toxicity. It is intended to help understand, manage, and plan for the risks of cardiac toxicity.
This document is a webinar presentation on managing drug shortages for colorectal cancer medications. It provides background on the increasing issue of drug shortages in the US, with over 200 shortages reported annually since 2010. For colorectal cancer specifically, 6 of the 9 main drugs used to treat the disease have been in shortage in the past 2 years, including 5-FU, leucovorin, and irinotecan. The presentation discusses reasons for shortages like manufacturing and supply chain issues, and strategies used by health systems and government agencies to help mitigate shortages, such as improved communication and expediting regulatory reviews of alternative products.
Virtual Workshop Innovative Approaches to Drug Safety 2019Arete-Zoe, LLC
The current practice of pharmacovigilance is fraught with challenges and limitations. Still, new technologies, perspectives, and concerns are shaping the way stakeholders will need to conduct this crucial activity in the coming years. You are cordially invited to join our workshop on the future of pharmacovigilance. We offer you an opportunity to participate in a robust, informative, and professional discussion about the future of pharmacovigilance. We seek your perspectives on the issues before us today and how they will influence the drug safety environment in the 2020s.
We understand the challenges and limitations of the current ways to conduct the business of pharmacovigilance and seek your perspective to achieve broader consensus. Topics of interest include the role of stakeholders in shaping the informational needs, system responsiveness, production of real-world evidence, incentives and barriers to investment
into automation and AI tools, the monetary value of safety information, patient privacy issues, and innovative approaches toward generating evidence.
Virios Therapeutics is a clinical-stage biotechnology company focused on advancing novel, dual mechanism antiviral therapies to treat conditions associated with virally triggered or maintained immune responses, such as Fibromyalgia (“FM”). Immune responses related to the activation of tissue resident Herpes Simplex Virus-1 (“HSV-1”) have been postulated as a potential root cause triggering and/or sustaining chronic illnesses such as FM, irritable bowel disease (“IBS”), and chronic fatigue syndrome, all of which can be characterized by waxing and waning symptom flare-ups with no obvious etiology. Virios’ lead development candidate (“IMC-1”) is a novel, proprietary, fixed dose combination of famciclovir and celecoxib designed to synergistically suppress HSV-1 replication, with the end goal of reducing virally promoted disease symptoms.
The document discusses prescription drug benefit mandates under the Affordable Care Act and strategies for designing non-discriminatory health plan formularies. It outlines requirements for covering essential health benefits, including prescription drugs, and prohibitions against denying coverage or setting discriminatory benefits based on health conditions. The document provides an agenda for a meeting on formulary drug reviews, outlining tools to assess formulary compliance with non-discrimination standards.
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
BioVie Inc. (OTCQB: BIVI) is a clinical-stage company developing innovative drug therapies for liver disease. The Company’s drug candidate, BIV201 (continuous infusion terlipressin), has an Orphan Drug designation for the treatment of refractory ascites, FDA Fast Track status, and US patent pending. BIV201 also has an Orphan Drug designation for the treatment of hepatorenal syndrome (HRS). The active agent in BIV201, terlipressin, is approved for use in about 40 countries for the treatment of related complications of advanced liver cirrhosis but is not available in the US or Japan. The FDA has never approved terlipressin. BioVie is targeting this landmark achievement.
Visit BIVIinfo.com to learn more.
Tiziana Life Sciences presented an overview of their transformational immunotherapy platform enabling alternative routes of administration. Their proprietary technologies allow for oral, nasal, and inhalation delivery of antibodies, which currently require IV administration. They highlighted clinical progress including trials of their lead asset foralumab administered intranasally for progressive MS and COVID-19. Results demonstrated safety and positive clinical responses. Tiziana is also developing other pipeline assets and expanding their management team and scientific advisory board.
biOasis Technologies, Inc. (BTI.TSXV) Updated Presentation Q1 2015graemedick
- biOasis has discovered a natural solution to deliver drugs across the blood brain barrier (BBB) using melanotransferrin (MTf), a human protein that transports molecules into the brain.
- Their Transcend drug delivery platform uses MTf to link therapeutic compounds to transport medicines for various central nervous system (CNS) diseases that currently have limited treatment options due to the BBB.
- Preclinical studies show MTf conjugates significantly increase brain delivery and activity of attached compounds like antibodies, enzymes and chemotherapy drugs.
This document discusses C-SPARQL, an extension of SPARQL 1.1 for querying and reasoning over RDF streams. It introduces a running example of social network data streams and presents the key features of C-SPARQL, including registering continuous queries over streams, using windows to define the triples available for querying, chaining queries together, and accessing background information. C-SPARQL allows deriving new information by reasoning over streaming and background data.
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members’ social media posts.
NE3107 is a small molecule in Phase 3 clinical trials for Alzheimer's disease and Parkinson's disease. It works by inhibiting neuroinflammation and insulin resistance, two key drivers of cognitive decline. A Phase 3 trial for Alzheimer's is underway testing NE3107's ability to slow cognitive decline compared to placebo. Preclinical studies show NE3107 reduces inflammation, enhances insulin sensitivity, and has neuroprotective effects, supporting its potential in neurodegenerative diseases. A Phase 2 trial will assess NE3107's activity and safety when combined with L-dopa for Parkinson's disease.
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
Lantern Pharma leverages AI, machine learning and genomic data to transform oncology drug development. Their RADR platform identifies patient subgroups likely to respond to drug candidates to increase success rates. They are developing multiple oncology drug programs including LP-100 for prostate cancer through a precision trial. LP-300 targets never-smoker NSCLC adenocarcinoma based on prior data and biomarker studies. Lantern aims to reduce costs and time through AI-enabled approaches.
One in 8 U.S. women will develop invasive breast cancer over her lifetime, with approximately 266,000 new breast cancer patients and 3.1 million breast cancer survivors in 2018. Following breast cancer surgery in the adjuvant setting, a HER2/neu 3+ patient typically receives Herceptin® in the first year, with the hope that their breast cancer will not recur, and with the odds of recurrence slowly decreasing over the first 5 years after surgery. Herceptin® has been shown to reduce recurrence rates from 25% to 12% in the adjuvant setting. In the neoadjuvant setting, a patient receives treatment before surgery and based on the results of a biopsy at surgery, will receive the same or more potent treatment after surgery. Kadcyla® has been shown to reduce recurrence rates from 22% to 11% in the neoadjuvant setting. Accordingly, we believe that GP2 may be used to address the 50% of recurring patients who do not respond to either Herceptin® or Kadcyla®.
This report examines CMS's oversight of Medicare Part D beneficiaries who receive opioid prescriptions and providers who prescribe opioids to these beneficiaries. It finds that while CMS provides guidance to Part D plan sponsors on monitoring beneficiaries at high risk of opioid overuse, it lacks complete data on the full population of beneficiaries at risk. It also finds that CMS oversees prescribing through its contractor NBI MEDIC but does not specifically analyze opioid prescription data or require reporting on actions taken regarding inappropriate opioid prescribing. The report concludes that CMS needs more comprehensive oversight to reduce the risks of opioid misuse, overdose, and inappropriate prescribing among Medicare beneficiaries.
This document provides an overview of drug courts in the United States, including their background and effectiveness. It discusses how drug courts work, their growth across the country since 1989, and their goal of reducing drug use and recidivism among nonviolent offenders through intensive supervision and court-mandated drug treatment. It also reviews studies that have found drug courts can reduce recidivism and describes the federal grant program that has helped fund drug courts since 1994, allocating over $530 million. Issues like how to measure effectiveness and whether more serious offenders could be included are discussed.
Virios Therapeutics is a clinical-stage biotechnology company focused on advancing novel, dual mechanism antiviral therapies to treat conditions associated with virally triggered or maintained immune responses, such as Fibromyalgia (“FM”). Immune responses related to the activation of tissue resident Herpes Simplex Virus-1 (“HSV-1”) have been postulated as a potential root cause triggering and/or sustaining chronic illnesses such as FM, irritable bowel disease (“IBS”), and chronic fatigue syndrome, all of which can be characterized by waxing and waning symptom flare-ups with no obvious etiology. Virios’ lead development candidate (“IMC-1”) is a novel, proprietary, fixed dose combination of famciclovir and celecoxib designed to synergistically suppress HSV-1 replication, with the end goal of reducing virally promoted disease symptoms.
This document summarizes a presentation on using prescription drug data to limit misuse and abuse by third-party payers. The presentation features speakers from myMatrixx and Express Scripts discussing how data from sources like prescription drug monitoring programs, the DEA, NPPES, proprietary databases, and pharmaceutical manufacturers can be mined and analyzed to identify problematic prescribing patterns, problem geographic areas, and individual doctors who may be recklessly prescribing controlled substances. The goal is to organize collaboration between private and public agencies to help address the epidemic of prescription drug abuse.
Reviva Pharmaceuticals Holdings Inc is a clinical development pharmaceutical company. It is developing a portfolio of internally discovered therapies that address unmet medical needs in the areas of central nervous system, cardiovascular, metabolic and inflammatory diseases.
Reviva Pharmaceuticals Holdings Inc is a clinical development pharmaceutical company. It is developing a portfolio of internally discovered therapies that address unmet medical needs in the areas of central nervous system, cardiovascular, metabolic and inflammatory diseases.
This document summarizes a report on cardiac toxicity. Cardiac toxicity, where drugs cause heart damage, has led to 28% of drug withdrawals over the last 30 years in the US. If cardiac toxicity is found during drug development or after launch, it can terminate development programs or require drugs to be withdrawn from the market. The report covers the physiology, pharmacology, clinical testing, regulation, and commercial implications of cardiac toxicity. It is intended to help understand, manage, and plan for the risks of cardiac toxicity.
This document is a webinar presentation on managing drug shortages for colorectal cancer medications. It provides background on the increasing issue of drug shortages in the US, with over 200 shortages reported annually since 2010. For colorectal cancer specifically, 6 of the 9 main drugs used to treat the disease have been in shortage in the past 2 years, including 5-FU, leucovorin, and irinotecan. The presentation discusses reasons for shortages like manufacturing and supply chain issues, and strategies used by health systems and government agencies to help mitigate shortages, such as improved communication and expediting regulatory reviews of alternative products.
Virtual Workshop Innovative Approaches to Drug Safety 2019Arete-Zoe, LLC
The current practice of pharmacovigilance is fraught with challenges and limitations. Still, new technologies, perspectives, and concerns are shaping the way stakeholders will need to conduct this crucial activity in the coming years. You are cordially invited to join our workshop on the future of pharmacovigilance. We offer you an opportunity to participate in a robust, informative, and professional discussion about the future of pharmacovigilance. We seek your perspectives on the issues before us today and how they will influence the drug safety environment in the 2020s.
We understand the challenges and limitations of the current ways to conduct the business of pharmacovigilance and seek your perspective to achieve broader consensus. Topics of interest include the role of stakeholders in shaping the informational needs, system responsiveness, production of real-world evidence, incentives and barriers to investment
into automation and AI tools, the monetary value of safety information, patient privacy issues, and innovative approaches toward generating evidence.
Virios Therapeutics is a clinical-stage biotechnology company focused on advancing novel, dual mechanism antiviral therapies to treat conditions associated with virally triggered or maintained immune responses, such as Fibromyalgia (“FM”). Immune responses related to the activation of tissue resident Herpes Simplex Virus-1 (“HSV-1”) have been postulated as a potential root cause triggering and/or sustaining chronic illnesses such as FM, irritable bowel disease (“IBS”), and chronic fatigue syndrome, all of which can be characterized by waxing and waning symptom flare-ups with no obvious etiology. Virios’ lead development candidate (“IMC-1”) is a novel, proprietary, fixed dose combination of famciclovir and celecoxib designed to synergistically suppress HSV-1 replication, with the end goal of reducing virally promoted disease symptoms.
The document discusses prescription drug benefit mandates under the Affordable Care Act and strategies for designing non-discriminatory health plan formularies. It outlines requirements for covering essential health benefits, including prescription drugs, and prohibitions against denying coverage or setting discriminatory benefits based on health conditions. The document provides an agenda for a meeting on formulary drug reviews, outlining tools to assess formulary compliance with non-discrimination standards.
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
BioVie Inc. (OTCQB: BIVI) is a clinical-stage company developing innovative drug therapies for liver disease. The Company’s drug candidate, BIV201 (continuous infusion terlipressin), has an Orphan Drug designation for the treatment of refractory ascites, FDA Fast Track status, and US patent pending. BIV201 also has an Orphan Drug designation for the treatment of hepatorenal syndrome (HRS). The active agent in BIV201, terlipressin, is approved for use in about 40 countries for the treatment of related complications of advanced liver cirrhosis but is not available in the US or Japan. The FDA has never approved terlipressin. BioVie is targeting this landmark achievement.
Visit BIVIinfo.com to learn more.
Tiziana Life Sciences presented an overview of their transformational immunotherapy platform enabling alternative routes of administration. Their proprietary technologies allow for oral, nasal, and inhalation delivery of antibodies, which currently require IV administration. They highlighted clinical progress including trials of their lead asset foralumab administered intranasally for progressive MS and COVID-19. Results demonstrated safety and positive clinical responses. Tiziana is also developing other pipeline assets and expanding their management team and scientific advisory board.
biOasis Technologies, Inc. (BTI.TSXV) Updated Presentation Q1 2015graemedick
- biOasis has discovered a natural solution to deliver drugs across the blood brain barrier (BBB) using melanotransferrin (MTf), a human protein that transports molecules into the brain.
- Their Transcend drug delivery platform uses MTf to link therapeutic compounds to transport medicines for various central nervous system (CNS) diseases that currently have limited treatment options due to the BBB.
- Preclinical studies show MTf conjugates significantly increase brain delivery and activity of attached compounds like antibodies, enzymes and chemotherapy drugs.
This document discusses C-SPARQL, an extension of SPARQL 1.1 for querying and reasoning over RDF streams. It introduces a running example of social network data streams and presents the key features of C-SPARQL, including registering continuous queries over streams, using windows to define the triples available for querying, chaining queries together, and accessing background information. C-SPARQL allows deriving new information by reasoning over streaming and background data.
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members’ social media posts.
Big Data Challenges and Trust Management: A Personal Perspective
A tutorial presented by Dr. Krishnaprasad Thirunarayan at the International Conference on Collaboration Technologies and Systems 2016 (CTS 2016)
https://www.youtube.com/watch?v=uBijGs1NJCE&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=13
Semantic and AI research communities have a strong body of work focuses on extracting facts from the web automatically and represent them in a graph based representation. NELL and Knowledge Vault are two prominent knowledge graphs of that kind. However, due to the inherent noise of the web the resulting knowledge also contain noisy data. With the huge volume of the facts extracted from the web, it is impractical to use traditional reasoning approaches to capture the inconsistencies in these knowledge graphs. This work addresses this issue by using semantics in the form of schema knowledge together with statistics in the form of confidence value of facts derived from information extraction techniques. They use probabilistic soft logic which is a recently introduced statistical learning approach which allows to assign weights to the logical statement and their dependencies. The weighted soft logic rules are represented in a probabilistic graphical model with their dependencies to identify the different interpretations of a KG and pick the most consistent KG.
References
Pujara, Jay, et al. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36.1 (2015): 65-74.
Pujara, Jay, et al. "Knowledge graph identification." International Semantic Web Conference. Springer Berlin Heidelberg, 2013.
Lise Getoor “Combining Statistics and Semantics to Turn Data into Knowledge” ESWC Keynote 2015
In this chapter the author want to find out, how can an average human being can become an Expert in a specific field, and He highlights the common traits of the experts such as:
expert see the world differently, which the non-experts can’t see
An Expert in a specific field has a superior memory for the details of that field
Most importantly experts overcomes the brain’s most famous constraint “7”
Author says that in the field of remembering an average human can hold upto 7 plus or minus 2 digits in his brain at time. This the capacity of our short term memories by which we are limited but an expert is not limited by this constraint. When an expert look at a number, he does not see just the number rather he sees a memory or an image from the past such as Birth date or any memory which is related to the number. In the chapter author explains this difference between an average human and expert more clearly with examples such as chicken sexers and Swat officers
https://www.youtube.com/watch?v=fmZDRL9P-v4&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=9
To make an autonomous vehicle more cognitive, it needs the implementation of advanced cognition theories and AI theories. In this work, we firstly make a brief overview of current advanced theories of cognition in Psychology and Computer Science. Then we mainly analyze and compare the architectures of the autonomous vehicles winning DARPA Challenges. The layout of sensors and the design of software system are critical to the winning autonomous vehicles. By comparing different autonomous vehicles, we find some common points shared among them and more differences due to the various sensors layouts and the difference among cognition architectures, which could give some valuable directions to the researchers in both computer science and cognition fields. Then we will link decision-making to intelligence decision-making and its algorithm using example.
Fuzzy modeling is a powerful approach found by Zadeh for the modeling of complex and uncertain systems [2]. Fuzzy logic has a distinctive advantage where the precise definition of a control process is unachievable. Fuzzy models have the ability to establish a relationship between input and output variables by employing predefined rules. The technique provides simple solutions which are based on natural language statements. Fuzzy logic takes the inputs and outputs in the form of fuzzy sets where each set contains elements that have varying degrees of membership. A fuzzy set then enables transforming real numbers to the membership degrees changing from 0 to 1. Fuzzy rules relate input variables to output variables. These rules represent the expert knowledge in the system. Indeed, the intuition behind fuzzy logic is, it works with perception-based data instead of measurement-based which are crisp and numeric. Hence, it tries to capture how human use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Perceptions in this manner are inherently imprecise when compared to crisp values, for example, a human might express his intuition about the weather as being not very hot while a sensor would read the heat in degrees and give us a crisp value. Therefore, perceptions are very subjective and reflect the partiality of human concepts.
In 2001, Prof. Zadeh proposed his computational theory of perceptions (CTP) where the objects of computations are words and propositions drawn from natural language rather than crisp numeric values. The idea of the theory came due to the unavailability of a methodology for reasoning and computing with perceptions rather than measurements. Hence, the CPT was the ground for allowing a computer to make subjective judgments which often refered as perceptual computing.
E.H. Mamdani, Application of fuzzy algorithms for control of simple dynamic plant, in: Proceedings of the Institution of Electrical Engineers, IET, 1974, pp. 1585-1588.
Zadeh, Lotfi A. "Fuzzy sets." Information and control 8, no. 3 (1965): 338-353.
Zadeh, Lotfi A. "A new direction in AI: Toward a computational theory of perceptions." AI magazine 22, no. 1 (2001): 73.
https://www.youtube.com/watch?v=wbXEXGT3I9I&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=8
Link of video:
https://www.youtube.com/watch?v=wbXEXGT3I9I
This is a review of the keynote presented by Eric Horvitz, Managing Director, Microsoft, Redmond.
This keynote was presented at Computing Community Consortium in Washington DC on June-07-2016.
Eric has discussed about 3 things in his keynote: Healthcare, Agriculture and Transport.
Mainly he has focussed on Health care.
The goal of AI
Broad Spectrum of Opportunities for AI
Healthcare
Sciences
Transportation
Agriculture
Sustainability
Education
Governance
Criminal justice
Privacy & security
Emergency management
A work conducted in John Hopkins University
References:
http://research.microsoft.com/en-us/um/people/horvitz/AI_supporting_people_and_society_Eric_Horvitz.pdf
https://www.youtube.com/watch?v=rek3jjbYRLo
https://en.wikipedia.org/wiki/Artificial_intelligence
https://en.wikipedia.org/wiki/AI_winter
http://research.microsoft.com/en-us/um/people/horvitz/
Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.
Link to the paper - http://knoesis.org/?q=node/2754
Gang affiliates have joined the masses who use social media to share thoughts and actions. Perhaps paradoxically, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation gang related crimes and activities. This talk discusses our efforts in analyzing street gangs on twitter, with an emphasis on discovering their profiles. Our approach, which uses deep learning to embed signals in tweet language, images, shared YouTube links, and emoji use into a vector space for machine learning classifiers, recovers gang member profiles with promising accuracy and a low false positive rate.
The document discusses cognitive theory of dreaming, which interprets dreams based on quantitative analysis of thoughts, images, and sensations occurring during sleep. According to cognitive theory, dreams reveal conceptions of self, others, the world, penalties, and conflicts that shape behavior when awake. The theory proposed by Calvin Hall is that dreams help shape ideas about the world. Additional sections discuss how sleep processes memories by extracting rules and insights, integrating new and old memories, and imagining possible futures to create meaning. Analyzing dreams may help understand conditions like coma and mental illness.
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
The document describes research on implicit entity linking in tweets. It discusses how tweets often contain implicit mentions of entities without explicitly naming them, and presents a knowledge-driven approach to identify these implicit entities. The approach builds entity models using factual knowledge from Wikipedia and contextual knowledge from tweets, and then applies a two-step process of candidate selection and disambiguation to link implicit entity mentions to candidate entities. Evaluation on movie and book tweets shows the approach achieves over 60% accuracy in linking implicit entities.
This document discusses an approach to semantics-based machine perception using bit vector encodings. It summarizes key aspects of human perception from cognitive theories, including the perception cycle of explanation and discrimination. The approach translates background knowledge and sensor observations into bit vector representations to efficiently execute explanation and discrimination via bitwise operations. This enables resource-constrained devices like smartphones to perform machine perception at the edge. The technique could enable applications in healthcare domains like cardiology by generating personalized questions or sensing from passive sensor data to detect health conditions.
The document discusses integrating sensor and social data to understand city events. It describes collecting data from multiple sources, including sensors and social media. Statistical models are used to analyze the sensor data and identify anomalies, which are then correlated with events extracted from social media using spatial and temporal proximity. The approach is evaluated on traffic data from San Francisco, integrating data from traffic sensors and Twitter to extract and corroborate traffic events.
With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.
https://www.youtube.com/watch?v=b5qR4urr0vU&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=11
A mental representation or cognitive representation is a hypothetical internal cognitive symbol that represents external reality[1], or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this”[3]. To define the “Human Mental Representation”, four concepts have been described; Similarity, Analogy, Relationships at the Heart of Semantic Web. Similarity is defined as “learning information about one is generally true of the other”, and this becomes more and more true as the probability that the two causal/source variables is the same increases. The relationships used identifying similarities differs between experts and novices, with novices using surface features and experts using deeper structural relationships. Similarly, people relied on similarity mappings when the relational roles were more complex.
The purpose of categorization is twofold, to be able to infer the properties of the entity and to adapt the category itself. This description is essentially Piaget’s theory of development through assimilation and accommodation. Communication is similar to categorization, but rather than resolving for oneself, the issue is resolving new or developing shared concepts between people, which relates to many of the psycholinguistic conceptual grounding discussions (i.e., Herb Clark). Analogy is a special kind of similarity. Two situations are analogous if they share a common pattern of relationships among their constituent elements even though the elements themselves differ across the two situations. Typically, one analog, termed the source or base, is more familiar or better understood than the second analog, termed the target” (p. 117). Therefore, theoretical models of analogical inference need to focus on binding and mapping.
We explained the “Knowledge Representation”, and in the end, We provided the examples of “ Ontology and Knowledge Base” from Relationships at the Heart of Semantic Web p:15 [2].
References:
1- Chapters: 2, 3, 4, 6, Book: The Cambridge Handbook of Thinking and Reasoning (pp. 117-142). New York: Cambridge University Press. By By: Keith J. Holyoak and Robert G. Morrison
2- Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, Book Title: Enhancing the Power of the Internet. By Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap
3- Marr, David (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press. ISBN 978-0262514620
This document summarizes a presentation on insights from state policies and interventions to curb prescription drug overdoses. It describes several interventions:
1) PRIMUM, a system in North Carolina that alerts prescribers to patients' risk of misusing or abusing opioids at the point of care.
2) A project in Rhode Island that developed protocols to improve opioid prescription safety for trauma patients, including alerts if prescriptions exceed dosage thresholds and requiring naloxone co-prescriptions.
3) A study in Pennsylvania that used Medicaid claims data to identify risk factors for opioid overdoses, such as high dosage and multiple prescribers/pharmacies, to target high-risk patients.
This document summarizes a presentation on urine drug testing and monitoring prescription drug use. It discusses how prescription drug monitoring programs identify, investigate, and address fraud, waste and abuse related to prescription drug use and urine drug testing. It outlines trends seen in urine drug testing results that indicate issues with adherence, illicit drug use, and inconsistencies between prescribed medications and test results. Best practices are discussed for utilizing utilization review, case management, and other tools to help ensure appropriate use of medications and compliance with treatment regimens. The impacts of compounds, opioids, and long term opioid use are also addressed.
This document outlines efforts by several states to leverage prescription drug monitoring program (PDMP) data as public health surveillance tools through CDC's Prevention Boost grant program. It describes Oklahoma's program which expanded PDMP data sharing and used the data for epidemiological analyses to monitor trends in high-risk prescribing behaviors and health outcomes. It also discusses Utah's program which evaluated causes of prescription opioid deaths using PDMP data and supported several pieces of legislation. Finally, it summarizes Kentucky's program which addressed the state's high prescription drug overdose rates by enhancing its PDMP and linking the data to other health databases for surveillance purposes.
Project Lazarus is a secular, non-profit organization that provides technical assistance to community groups and clinicians throughout North Carolina and beyond to prevent drug overdoses. It uses a five-step approach: 1) building community knowledge and coalitions, 2) epidemiologic monitoring, 3) prevention, 4) rescue, and 5) program evaluation. The organization works to reduce supply, demand, diversion and harm from prescription drug abuse through community awareness, strategic planning, and harm reduction programs.
Slides presented by me at the Korean-American Professional Association in Life Sciences (KAPAL) 5th Annual Meeting in Rockville, MD. Slides discuss the recent reorganization of the Center for Drug Evaluation and Research at the FDA
How to address privacy, ethical and regulatory issues: Examples in cognitive ...SharpBrains
How to address privacy, ethical and regulatory issues: Examples in cognitive enhancement, depression and ADHD
Dr. Karen Rommelfanger, Director of the Neuroethics Program at Emory University
Dr. Anna Wexler, Assistant Professor at the Perelman School of Medicine at UPenn
Jacqueline Studer, Senior VP and General Counsel of Akili Interactive Labs
Chaired by: Keith Epstein, Healthcare Practice Leader at Blue Heron
Slidedeck supporting presentation and discussion during the 2019 SharpBrains Virtual Summit: The Future of Brain Health (March 7-9th). Learn more at:
https://sharpbrains.com/summit-2019/
This document summarizes Pamela Gavin's presentation on an evidence-based approach to assuring value for orphan drugs at the CEPHT Conference in Toronto. It discusses the progress made since the 1983 Orphan Drug Act, including over 700 approved orphan drugs. It then introduces the NORD-Trio partnership, which aims to ensure adequate access to orphan drugs through a patient-centric technology platform that measures stakeholder performance, defines quality measures, and advocates for patients and high performance. Finally, it provides examples of how this partnership could study the hepatitis C treatment landscape using real-world data on outcomes, access, and affordability.
2012 How did Rx abuse become a National Epidemic in the USTeresa Miller
Information includes: definition of epidemic, abuse, how Rx abuse became an epidemic, national stats on emergency room visits, overdoses, ... who are the supplies, end users... misusers by population, cost to our society & economy, ....
This document summarizes a presentation on data-driven trends related to prescription drug abuse. It outlines national trends in doctor shopping, overdoses, drugged driving, and opioid/heroin overdose deaths. It also evaluates the effectiveness of some state laws and programs aimed at reducing doctor shopping and responding to overdoses. Some promising policy strategies discussed include reducing inappropriate prescribing, focusing on overdose response, improving prescription drug monitoring programs, and linking overdose victims to treatment.
Patient-centric social media for outcomes and pharmacovigilance consideration...Inspire
Through the use of de-identified Big Data from online patient forums open to healthcare providers, the pharmaceutical industry may glean useful insights into both the safety of existing products as well as future needs of patients. Post-marketing safety surveillance for pharmaceuticals currently relies on data from adverse event reports to companies or regulatory authorities, medical literature, and observational databases. Together these sources provide some insight into everyday product safety or risk, but the unique insight the patients themselves can offer is also highly desirable.
Using insights from a 2016 research project involving Inspire, GlaxoSmithKline (GSK) Pharmaceuticals, and Epidemico, an innovative informatics company, we are exploring the use of social listening data for pharmacovigilance and other R&D concerns. A core question is, “What valuable insights can we glean from social listening to help improve patients’ lives—whether through improved safety, more relevant clinical trials, or research and development of new treatment options?”
This document discusses the use of poison center data to track trends in prescription drug abuse and overdoses. It shows that between 1999-2011, prescription opioid exposures reported to poison centers increased over 150% in the US and 164% in Kentucky. Specific opioids like oxycodone, hydrocodone, and tramadol also increased substantially. Poison center data can provide real-time surveillance to identify emerging problems and formulate strategies to address them.
Evaluating and Investigating Drug Safety Signals with Public DatabasesPerficient
This document provides an overview of databases that can be used to evaluate and investigate drug safety signals. It discusses common language and concepts in pharmacovigilance like signal detection, evaluation, and prioritization. It also describes online free databases like CDC WONDER and EU-ADR that can be used to search for drug-event pairs and investigate potential safety signals. Finally, it outlines some fee-based databases like those from Group Health Cooperative and Kaiser Permanente that contain patient healthcare records that can be used to conduct observational studies.
Tackling the Opioid Problem - Analgesic Prescribing in the Emergency DepartmentSCGH ED CME
This document discusses the opioid crisis and approaches to pain management. It describes how Purdue Pharma aggressively marketed OxyContin in the 1990s, leading to widespread overprescription and misuse. This contributed significantly to the rise in opioid overdoses and deaths in the US. In response, pharmaceutical companies developed abuse-deterrent formulations of opioids like OxyContin and Targin to discourage tampering and injection. However, these formulations did not prove abuse-proof. The document advocates for careful opioid prescribing practices to limit diversion and abuse, including assessing risks, limiting durations, and involving specialist services. Non-opioid options like paracetamol, NSAIDs, and tramadol should be prioritized for mild-moderate pain
Visit : www.acriindia.com
ACRI is a leading pharmacovigilance training Institute in Bangalore.
ACRI creates a value add for every degree. Our PG course is diploma in clinical research and PG diploma in pharmavigilance are approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
EMR functionality for clinical research has long been dreamed about, but how close to reality is it actually? This presentation lays out some basic facts about the viability (or lack thereof) of using EMRs for Phase 1-3 clinical trials.
KCR: Post-Authorisation Safety Studies (PASS) - Is the Ongoing Surveillance a...KCR
This document discusses post-authorization safety studies (PASS) and ongoing safety surveillance. It provides background on the legal basis and guidelines for PASS from the EMA and ICH. It notes that about 25% of products under additional monitoring by EMA have PASS requirements. The number of studies registered in the EU PAS register has risen significantly in recent years. PASS can be either imposed as an obligation or conducted voluntarily. Key differences between PASS and pre-approval studies are also outlined. The role and significance of PASS is increasing as a way to decrease drug development costs and times while increasing the collection of real-world data.
A User’s Perspective: Somatic Variant Analysis in VarSeq 2.3.0Golden Helix
VarSeq 2.3.0 facilitates the evaluation of a multitude of somatic genomic variations with a more refined user interface to streamline variant evaluation. Our recent webcasts have shown the full range of these newly developed upgrades:
VarSeq 2.3.0: Supporting the Full Spectrum of Genomic Variation
VarSeq 2.3.0: New TSO-500 and Genomic Signature Support in VSClinical AMP
Now, we are showing it all in action from the user’s perspective. This webcast will provide a comprehensive demonstration of performing somatic variation analysis and reporting. We will review how to use workflow automation to expedite the NGS project creation process and report rendering. We will also demonstrate the streamlined capture of knowledge during variant evaluation by leveraging our clinical expert-curated interpretations with the Golden Helix Cancer Knowledge Base (CancerKB).
We hope you will join us to see VarSeq 2.3.0 from a user’s perspective, covering:
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Pharmacovigilance & Adverse drug reactionRahul Bhati
This document discusses pharmacovigilance and adverse drug reactions (ADRs). It begins by defining pharmacovigilance as the monitoring of drug safety, and describes how the thalidomide disaster in the 1960s prompted significant changes to drug safety systems worldwide. It then discusses various reasons for the need of pharmacovigilance like limited preclinical safety data and changing drug use patterns. The aims and methods of pharmacovigilance including spontaneous reporting, case studies, and periodic safety reports are summarized. It also provides an overview of the Pharmacovigilance Program of India and its goals of monitoring ADRs and ensuring drug benefits outweigh risks. Finally, it defines different types of ADRs and their
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Letter to MREC - application to conduct studyAzreen Aj
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Healthy Eating Habits:
Understanding Nutrition Labels: Teaches how to read and interpret food labels, focusing on serving sizes, calorie intake, and nutrients to limit or include.
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Benefits of Regular Exercise:
Physical Benefits: Discusses how exercise aids in weight management, muscle and bone health, cardiovascular health, and flexibility.
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Tips for Staying Active:
Encourages consistency, variety in exercises, setting realistic goals, and finding enjoyable activities to maintain motivation.
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Exploring Synthetic Cannabinoid Effects Using Web Forum Data
1. Exploring Synthetic Cannabinoid Effects
Using Web Forum Data
(NIDA National Early Warning System Network)
Robert G. Carlson1,2, Francois Lamy1,2, Amit Sheth2, Raminta
Daniulaityte1,2
1Center for Interventions, Treatment & Addictions Research
Wright State University Boonshoft School of Medicine, Dayton, OH
2Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of
Computer Science and Engineering, Wright State University, Dayton, OH, United States
ITAR
CTreatment & Addictions Research
Center for Interventions,
2. Acknowledgments
• R56 DA038366 “NIDA National Early Warning
System Network (iN3): An Innovative Approach”
• Robert G. Carlson1,2, PI; Amit Sheth2, PI; Edward Boyer3, PI;
Raminta Daniulaityte1,2, Co-I; Jeffrey Brent, 4 Co-I; Paul Wax,
Co-I4
• 1Center for Interventions, Treatment & Addictions Research
• Wright State University Boonshoft School of Medicine, Dayton, OH
• 2Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis),
Department of Computer Science and Engineering, Wright State University,
Dayton, OH, United States
• 3Department of Emergency Medicine, University of Massachusetts Medical
School, Worcester, MA, United States
• 4American College of Medical Toxicology, Phoenix, AZ, United States
3. Acknowledgments
• R01 DA039454 “Trending: Social Media Analysis to Monitor
Cannabis and Synthetic Cannabinoid Use”
• Raminta Daniulaityte1,2, PI; Amit Sheth2; PI; Edward Boyer, 3 Co-I;
Robert Carlson,1,2 Co-I; Ramzi Nahhas,4 Co-I; Silvia S. Martins, Co-I5
• 1Center for Interventions, Treatment & Addictions Research, Wright State University
Boonshoft School of Medicine, Dayton, OH
• 2Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis),
Department of Computer Science and Engineering, Wright State University, Dayton,
OH, United States
• 3Department of Emergency Medicine, University of Massachusetts Medical School,
Worcester, MA, United States
• 4Center for Global Health, Department of Community Health, Wright State University
Boonshoft School of Medicine, Dayton, OH
• 5Department of Epidemiology, Columbia University Mailman School of Public Health,
New York, NY
4. Acknowledgments
• The content is solely the responsibility of the authors and
does not necessarily represent the official views of the
National Institute on Drug Abuse or the National
Institutes of Health.
• The authors have no Conflict of Interest to declare.
5. R56 Specific Aims
• 1) Identify new episodes of emerging drug use in
multiple community-level indicators;
• 2) Disseminate information about occurrence,
identity, clinical, and adverse effects of emerging
drug use;
• TWO DATA STREAMS:
–1) Data on Synthetic Cannabinoids and other Novel
Synthetics from 41 Emergency Department sites across
the US (American College of Medical Toxicology);
–2) Data from Twitter and drug Web forums.
6. Rationale to use Web-forum posts
• SCRA users are difficult to reach;
• Most knowledge comes from Poison Control Centers
or Emergency Rooms;
• Mining drug-focused web-forums offers the possibility
to collect data from users freely expressing their
opinions and experiences;
• PRESENTATION AIM: We present eDrugTrends data
on Web forum posts related to Synthetic
Cannabinoids and their effects.
7. Data Sources
• We deployed the eDrugTrends Web forum data
collection and processing features;
• Data were collected from Bluelight, Drugs-forum, and
Reddit;
• Bluelight website is a partner in the R01 study.
9. Drug Abuse Ontology (DAO)
• Ontology is a conceptualization of all the elements that
belong to a specific domain;
• DAO currently encompasses 944 drug-related terms
(184 SCRA terms) as well as 419 positive and
negative drug-related effect terms;
• DAO Enables entity recognition within posts and co-
occurrences of several concepts.
10. Type of
keywords
Individual Entity Examples
Chemical
name
JWH-018; JWH-073; CP-47,497; HU-210; WIN-55; JWH-200; CCH;
JWH-250; AM-2201; JWH-210; JWH-122; JWH-203; AM-2233;
JWH-019; UR-144; APICA; FUBINACA; CHMINACA; PB-22;
PINACA; AKB-48; THJ-018; STS-135; BB-22; BB-25; 5F-MN; 5F-
AKB; 5F-AMB; 5F-ADB; PB-22; NM-2201; SDB-006; 5F-SDB…
Commercial
names
“Black Mamba”; “Tribal Warrior”; “Mr. Kush”; “Mad Hatter”; “Afghan
Black”; “Atomic Bomb”*; Clockwork Orange”*; “Bamboo”*; “Voodoo”*;
“Ultimate Warrior”; “EX-SES”; “Blue Cheese”*; “Bizzaro”…
General
names
“Spice”; “K2”; “noid”; “SCRA”; “synthetic cannabis”; “pot-pourri”;
“herbal incense”; “fake weed”…
*Because keywords could be used in general discussion (e.g., “Clockwork
Orange” as a reference to Stanley Kubrick movie), we ensured that this
mention was related to SCRA by combining these searched terms with the
term “cannabinoid”.
Ontology-based SCRA Search Terms
11. Ontology-based Side-effect Search Terms
Categories Individual Entities Examples
Acute
Respiratory
Acute Exacerbation, Apnea, Bronchitis, Bronchospasm, Dyspnea,
Pneumonitis, Respiratory Depression, Slow Breathing, Shallow
Breathing, Suffocation,….
Chronic
Respiratory
Asthma, Chronic Cough, Cough all the time, Lung Cancer
Acute
Cognitive
Alertness, Anterograde Amnesia, Auditory Distortions, Closed-Eye
Visualizations, Confusion, Diplopia, Hallucination, Impaired
Reflexes….
Chronic
Cognitive
Memory Dysfunction, Amnesia.
Acute
Nervous
System
Anxiety, Ataxia, Clonus, Dizziness, Drowsiness, Euphoria,
Excitoxicity, Exhilaration, Headache, Pain, Panic Attack, Sedation,
Seizure,….
Social
Related
Aggressiveness, Crime, Decreased Work Performance, Empathy,
Felony, Unsafe sex…
*Misspellings are frequent in web-forum posts. Hence, some side effect keywords were collected using
“fuzzy query” based on Levenshtein edit distance. This type of query guaranties that misspelled words
(such as “siezure” instead of “seizure”) would be captured by our data query.
12. Data collection
• The eDrugTrends platform extracted 19,700,000+
drug-related posts from our data sources from
01/01/2008 until 09/30/2015;
• 43,506 posts containing SCRA Search Terms
found;
–Reddit: 25,981; Drugs-forum: 9,271; Bluelight:
8,254
• 19,728 users shared their thoughts and
experiences on SCRA online.
13. Distribution of SCRA posts over time
0
500
1000
1500
2000
2500
Jan-08 May-09 Sep-10 Feb-12 Jun-13 Nov-14
Posts Users
14. Frequency of SCRA-related Effects
• To ensure recognized effects are related to SCRAs,
we isolated 18,617 posts (n=42.8%) containing no
references to other drugs;
• Why? Because if not cannot presently determine if
effects are related to SCRAs or another drug (s).
• Among these, 4,638 posts (n=24.9%) contained one
or several effects.
–42.4% only negative effect(s), 38.2% only positive
effect(s) and 19.4% both effects
17. An example of Entity Recognition
• Personally I found AM-2201 extremely trippy in
headspace and visual components at high doses.
[…] That was the scariest moment I've ever had on
a noid EVER out of my 6 years of smoking noids.
I sweated profusely from head to toe, heart raced
at about 150-155 BPM, and I was having a major
anxiety attack (and I don't have anxiety
problems). […] Because of the extremely addictive
properties of AM-2201 I went through 300mg in 5
days. […] I found the effects of AM-2201 not only
psychedelic but very dissociating mentally from
reality.
18. SCRA Positive and Negative Effects Posts:
changes over time
-20
0
20
40
60
80
100
120
Dec-07 Apr-09 Sep-10 Jan-12 Jun-13 Oct-14
Chart Title
Negative Positive Positive+Negative
19. Discussion
• Decrease in the number of posts starting end of 2012
corresponds with the overall trend of SCRA use as
identified in other sources (NIH, 2015; UNODC,
2016);
• Cannot conclude that SCRA use is decreasing from
an Epi standpoint. Only that people who write to drug
web forums are doing so less.
• Effects extracted from web data are similar to clinical
data (Castellanos & Gralnik, 2016);
• Ability to obtain information about patterns of SCRA
use among a hidden population.
• Potential for much deeper analyses of texts.
20. Limitations
• Entity Recognition based on ”bag of words”
rather than semantic relation;
• Demographic and Geographic information
rarely displayed by web-forum users;
• Polydrug use represents major challenges for
Entity Relation Extraction.
21. Future Steps
• Entity relation extraction to ensure the semantic
relation between ontological concepts;
• E.G., Entity recognition of effects based on body
parts/organs to capture specificity of negative effects;
• Implement Machine Learning trained by domain
experts to increase information extraction accuracy.
(Disambiguation issues)
• Advancements in technical dimensions will enable
addressing a wide array of drug abuse research
questions, such as dose and route of administration,
addiction/withdrawal, and perceived need for
treatment.
• Web-based surveys will enable key insight into
demographics and geolocation.
22. THANK YOU
• Contact Details:
– robert.carlson@wright.edu
– francois.lamy@wright.edu
– Amit.sheth@wright.edu
– Raminta.daniulaityte@wright.edu
• References:
– Castellanos, Daniel et Gralnik, Leonard M. Synthetic cannabinoids 2015:
An update for pediatricians in clinical practice. World journal of clinical
pediatrics, 2016, vol. 5, no 1, p. 16.
– Johnston, Lloyd D., O'Malley Patrick M., Bachman, Jerald G., et al.
Monitoring the Future: National Survey Results on Drug Use, 1975-2005.
Volume 1: Secondary School Students, 2005. NIH Publication No. 06-
5883. National Institute on Drug Abuse (NIDA), 2006.
– United Nations Office on Drugs and Crime, World Drug Report 2015
(United Nations publication, Sales No. E.15.XI.6).
Editor's Notes
To analyze web forums data, we first collected posts from our data sources. We, then, stored them and use the Kibana software to explore and visualize drug-related posts. Kibana is a big data visualization and exploration tool. To extract relevant information on SCRA, we design and use a knowledge-based ontology to match entities of interest in the collected posts. We, then, analyze the frequency distribution of these entities.