PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Optimization of Backpropagation for Early Detection of Diabetes Mellitus IJECEIAES
Diabetes mellitus is one of the urgent health problems in the world. Diabetes is a condition primarily defined by the level of hyperglycemia giving rise to risk of micro vascular damage. Those who suffer from this disease generally do not realize and tend to overlook the early symptoms. Late recognition of these early symptoms may drive the disease to a more concerning level. One solution to solve this problem is to create an application that may perform early detection of diabetes mellitus so that it does not grow larger. In this article, a new method in performing early detection of diabetes mellitus is suggested. This method is backpropagation with three optimization namely early initialization with Nguyen-Widrow algorithm, learning rate adaptive determination, and determination of weight change by applying momentum coefficient. The observation is conducted by collecting 150 data consisting of 79 diabetes mellitus patient and 71 non diabetes mellitus patient data. The result of this study is the suggested algorithm succeeds in detecting diabetes mellitus with accuracy rate of 99.33%. Optimized backpropagation algorithm may allow the training process goes 12.4 times faster than standard backpropagation.
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Optimization of Backpropagation for Early Detection of Diabetes Mellitus IJECEIAES
Diabetes mellitus is one of the urgent health problems in the world. Diabetes is a condition primarily defined by the level of hyperglycemia giving rise to risk of micro vascular damage. Those who suffer from this disease generally do not realize and tend to overlook the early symptoms. Late recognition of these early symptoms may drive the disease to a more concerning level. One solution to solve this problem is to create an application that may perform early detection of diabetes mellitus so that it does not grow larger. In this article, a new method in performing early detection of diabetes mellitus is suggested. This method is backpropagation with three optimization namely early initialization with Nguyen-Widrow algorithm, learning rate adaptive determination, and determination of weight change by applying momentum coefficient. The observation is conducted by collecting 150 data consisting of 79 diabetes mellitus patient and 71 non diabetes mellitus patient data. The result of this study is the suggested algorithm succeeds in detecting diabetes mellitus with accuracy rate of 99.33%. Optimized backpropagation algorithm may allow the training process goes 12.4 times faster than standard backpropagation.
Cerebral infarction classification using multiple support vector machine with...journalBEEI
Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.
Theera-Ampornpunt N. Global or glocal e-Health approaches in Asia: what is new or next? Presented at: Globalizing Asia: Health Law, Governance, and Policy - Issues, Approaches, and Gaps!; 2012 Apr 16-18; Bangkok, Thailand.
Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine L...PurwonoPurwono4
Stroke is a disease caused by brain tissue damage because of blockage in the
cerebrovascular system that disrupts body sensory and motoric systems
Stroke disease is one of the highest death cause in the world. Data collection
from Electronic Health Records (EHR) is increasing and has been included
in the health service big data. It can be processed and analyzed using machine
learning to determine the risk group of stroke disease. Machine learning can
be used as a predictor of stroke causes, while the predictor clarifies the
influence of each cause factor of the disease. Our contribution in this research
is to evaluate Feyn Qlattice machine learning models to detect the influence
of stroke disease's main cause features. We attempt to obtain a correlation
between features of the stroke disease, especially on the gender as a feature,
whether any other features can influence the gender feature. This research
utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The
result implies that gender highly impacts age and hypertension on stroke
disease causes. Autorun in Feyn Qlattice model was run with ten epochs,
resulting in 17596 test models at 57s. Query string parameter that was focused
on age and hypertension features resulted in 1245 models at 4s. An increase
of accuracy was found in training metrics from 0.723 to 0.732 and in testing
metrics from 0.695 to 0.708. Evaluation results showed that the model is
reasonably good as a predictor of stroke disease, indicated with blue lines of
AUC in training and testing metrics close to ROC's left side peak curve.
Icbme2020- Use of neural network algorithms to predict arterial blood gas ite...Mohammad Sabouri
Use of neural network algorithms to predict arterial blood gas items in trauma victims
Milad Shayan
Mohammad Sabouri
Dr. Shahram Paydar
Leila Shayan
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
27th National and 5th International Conference of Biomedical Engineering
https://sites.google.com/view/acrrl/
http://icbme.ir/
An Experimental Study of Diabetes Disease Prediction System Using Classificat...IOSRjournaljce
Data mining means to the process of collecting, searching through, and analyzing a large amount of data in a database. Classification in one of the well-known data mining techniques for analyzing the performance of Naive Bayes, Random Forest, and Naïve Bayes tree (NB-Tree) classifier during the classification to improve precision, recall, f-measure, and accuracy. These three algorithms, of Naive Bayes, Random Forest, and NB-Tree are useful and efficient, has been tested in the medical dataset for diabetes disease and solving classification problem in data mining. In this paper, we compare the three different algorithms, and results indicate the Naive Bayes algorithms are able to achieve high accuracy rate along with minimum error rate when compared to other algorithms.
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 7, 2020
Presented at the 9th Thailand Pharmacy Congress: Smart Aging Life & Digital Pharmacy 4.0, The Pharmaceutical Association of Thailand under Royal Patronage on November 18, 2017.
Presented at the 9th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on March 4, 2019
Presented at the 8th Healthcare CIO Certificate Program, Ramathibodi Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University on March 12, 2018
Theera-Ampornpunt N, Kelley T, Ramly E, Shaw R, Khairat S, Sonnenberg FA. The paths toward informatics careers in the post-HITECT era [panel submission]. AMIA Annu Symp Proc. 2012 Nov:1565-7.
Theera-Ampornpunt N. Global or glocal e-Health approaches in Asia: what is new or next? Presented at: Globalizing Asia: Health Law, Governance, and Policy - Issues, Approaches, and Gaps!; 2012 Apr 16-18; Bangkok, Thailand.
Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine L...PurwonoPurwono4
Stroke is a disease caused by brain tissue damage because of blockage in the
cerebrovascular system that disrupts body sensory and motoric systems
Stroke disease is one of the highest death cause in the world. Data collection
from Electronic Health Records (EHR) is increasing and has been included
in the health service big data. It can be processed and analyzed using machine
learning to determine the risk group of stroke disease. Machine learning can
be used as a predictor of stroke causes, while the predictor clarifies the
influence of each cause factor of the disease. Our contribution in this research
is to evaluate Feyn Qlattice machine learning models to detect the influence
of stroke disease's main cause features. We attempt to obtain a correlation
between features of the stroke disease, especially on the gender as a feature,
whether any other features can influence the gender feature. This research
utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The
result implies that gender highly impacts age and hypertension on stroke
disease causes. Autorun in Feyn Qlattice model was run with ten epochs,
resulting in 17596 test models at 57s. Query string parameter that was focused
on age and hypertension features resulted in 1245 models at 4s. An increase
of accuracy was found in training metrics from 0.723 to 0.732 and in testing
metrics from 0.695 to 0.708. Evaluation results showed that the model is
reasonably good as a predictor of stroke disease, indicated with blue lines of
AUC in training and testing metrics close to ROC's left side peak curve.
Icbme2020- Use of neural network algorithms to predict arterial blood gas ite...Mohammad Sabouri
Use of neural network algorithms to predict arterial blood gas items in trauma victims
Milad Shayan
Mohammad Sabouri
Dr. Shahram Paydar
Leila Shayan
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
27th National and 5th International Conference of Biomedical Engineering
https://sites.google.com/view/acrrl/
http://icbme.ir/
An Experimental Study of Diabetes Disease Prediction System Using Classificat...IOSRjournaljce
Data mining means to the process of collecting, searching through, and analyzing a large amount of data in a database. Classification in one of the well-known data mining techniques for analyzing the performance of Naive Bayes, Random Forest, and Naïve Bayes tree (NB-Tree) classifier during the classification to improve precision, recall, f-measure, and accuracy. These three algorithms, of Naive Bayes, Random Forest, and NB-Tree are useful and efficient, has been tested in the medical dataset for diabetes disease and solving classification problem in data mining. In this paper, we compare the three different algorithms, and results indicate the Naive Bayes algorithms are able to achieve high accuracy rate along with minimum error rate when compared to other algorithms.
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 7, 2020
Presented at the 9th Thailand Pharmacy Congress: Smart Aging Life & Digital Pharmacy 4.0, The Pharmaceutical Association of Thailand under Royal Patronage on November 18, 2017.
Presented at the 9th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on March 4, 2019
Presented at the 8th Healthcare CIO Certificate Program, Ramathibodi Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University on March 12, 2018
Theera-Ampornpunt N, Kelley T, Ramly E, Shaw R, Khairat S, Sonnenberg FA. The paths toward informatics careers in the post-HITECT era [panel submission]. AMIA Annu Symp Proc. 2012 Nov:1565-7.
Informatics and Clinical Decision Support in Precision MedicineAndre Dekker
Talk given during http://www.miccai2015.org/ in Munich Germany. Part of the Satellite Workshop and Challenges in Imaging & Digital Pathology (https://wiki.cancerimagingarchive.net/x/JgM7AQ).
Artificial Organ Technology and Market Analysis 2017 Report by Yole Developpe...Yole Developpement
How will artificial organs revolutionize organ transplants and overcome shortages in the next 20 years?
FIVE OUT OF THE TEN LEADING CAUSES OF DEATH IN THE WORLD WILL BENEFIT FROM ARTIFICIAL ORGANS
Organ transplantation is often the only treatment for end-state organ failure, such as liver, kidney and heart failure. Tragically, most people on the waiting list die before they ever get an organ. Hence the dream of developing artificial organs made of electronic and mechanical parts has been around for decades. The first total artificial heart transplant was in the 1980s, yet since then few improvements have made these devices more efficient. Newcomers such as Carmat and Bivacor are aiming to change the paradigm from a single mechanical heart towards a smarter solution, with embedded sensors and intelligence.
The next wave of development came from the diabetes epidemic that affects every country, hitting more than 8% of the global population today. The artificial pancreas market will therefore experience a huge 49% compound annual growth rate (CAGR) over the next five years, to reach $1.3B in 2022. The next breakthrough to happen will come in 5-10 years, bringing artificial lungs and kidneys. The first commercially approved devices will be wearable systems such as the Wearable Artificial Kidney Foundation, Inc. (WAKFI) system.
More information on that report at http://www.i-micronews.com/reports.html
tranSMART Community Meeting 5-7 Nov 13 - Session 3: The TraIT user stories fo...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 3: The TraIT user stories for tranSMART
The TraIT user stories for tranSMART
Jan-Willem Boiten, TraIT
The Translational Research IT (TraIT) project in The Netherlands aims to organize, deploy, and manage a nationwide IT infrastructure for data and workflow management targeted specifically at the needs of translational research projects. tranSMART has been selected as the central data integration and browsing solution across the four major domains of translational research: clinical, imaging, biobanking and experimental (any-omics). For this purpose user stories from anticipated user projects are collected and mapped onto the current functionality of tranSMART. The gaps identified in this analysis are being tackled systematically as summarized in the TraIT development roadmap for tranSMART.
An OGMS-based Model for Clinical Informationoberkampf
This is the presentation of the paper presented on the early career symposium at the International Conference on Biomedical Ontology. http://www.unbsj.ca/sase/csas/data/ws/icbo2013/papers/ec/icbo2013_submission_56.pdf
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
Improving health care outcomes with responsible data scienceWessel Kraaij
Keynote presentation by Wessel Kraaij at the Dutch pattern recognition and impage processing society (NVPBV) 29/5/2018, Eindhoven.
This talk discusses
1. trends in health care and respondible data science and their intersection
2. Secure federated analytics on distributed data repositories
3. Generating clinically relevant hypotheses from patient forum discussions.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
Medical Informatics: Computational Analytics in HealthcareNUS-ISS
Presented by Dr Liu Nan, Senior Research Scientist and Principal Investigator, Singapore General Hospital at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.
I gave this talk in the "Presidential Symposium" at the annual meeting of the American Association of Physicists in Medicine, in Annaheim, California. The President of AAPM, Dr. Maryellen Giger, wanted some people to give some visionary talks. She invited (I kid you not) Foster, Gates, and Obama. Fortunately Bill and Barack had other commitments, so I did not need to share the time with them.
Data warehousing and analytics for healthcare in practiceDaniel Kapitan
While the promise and potential impact of data is large, there are many day-to-day challenges to implement a foundation to enable data-driven decisions in healthcare. Using the analytics adoption and maturity model as a framework, the different stages of how analytics is done in practice will be presented and discussed.
Guest lecture given at the Data Science Center, Technical University Eindhoven.
Developing and validating statistical models for clinical prediction and prog...Evangelos Kritsotakis
Talk on clinical prediction models presented at the Joint Seminar Series in Translational and Clinical Medicine organised by the University of Crete Medical School, the Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology Hellas (IMBB-FORTH), and the University of Crete Research Center (UCRC), Heraklion [online], Greece, April 7, 2021.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
Recent and Latest Advances in Oral and Maxillofacial surgery, Dr. Lidetu Afew...LIDETU AFEWORK
Every one should update himself according to the recent advances in every single profession/department. These are some of advancements We got in OMFS. We have also some latest advances and future advances under study that is going to be released in near future. BE HIGHTECH HIGH QUALITY UPDATED AND INFORMED PROFESSION.
Similar to MAASTRO Knowledge Engineering: The Fun(ction) of Medical Physics in Cancer Care (20)
MAASTRO clinic is hét instituut voor radiotherapie dat de bestraling van kankerpatiënten in de provincie Limburg verzorgt.
We doen dat vanuit hoofdvestiging Maastricht en op locatie in Venlo. MAASTRO werkt intensief samen met zowel het academisch ziekenhuis Maastricht (azM) als de Universiteit Maastricht (UM).
Een samenwerkingsverband waarin we wetenschappelijk onderzoek verrichten, met o.a het azM, UM en GROW.
En dát is belangrijk om te kunnen blijven
innoveren in radiotherapie.
MAASTRO takes on cancer together with the patient and his or her relatives. Our fight against cancer is based around radiotherapy, scientific research and education. View also our general corporate presentation (on the left).The central aim of MAASTRO Clinic’s strategy is “individualised radiotherapy”. "Heading the charge for our patients!" or watch the short animation movie about Maastro's vision for cancer treatment.
Carbonic Anhydrase IX: regulation and role in cancerMAASTRO clinic
Lecture by Prof. Silvia Pastorekova in the course: "Tumour Hypoxia: From Biology to Therapy III". For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Measuring of biological parameters of the tumor microenvironment – advantages...MAASTRO clinic
Lecture by Andreas Weltin in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III". For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Lecture by Dr. Supuran in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III".
For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Lecture by Prof. Lambin in the course: "Tumour Hypoxia: From Biology to Therapy III". For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Hypoxia as a target for personalized medicineMAASTRO clinic
Lecture by Prof. Hans Kaanders in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III". For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Global & local oxygen control in in vitro systemsMAASTRO clinic
Lecture by Humbert Flamm in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III".
For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Tumour Hypoxia - detection and prognostic significanceMAASTRO clinic
Lecture by Dr. Heidi Lyng in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III".
For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Biological responses to tumor hypoxia & their potential as therapeutic targetsMAASTRO clinic
Lecture by Dr. Brad Wouters in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III".
For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
Epigenetic Mechanisms Regulating the Cellular Response to HypoxiaMAASTRO clinic
Lecture by Prof. Lorenz Poellinger in the course: "Tumour Hypoxia: From Biology to Therapy III". For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
DISSERTATION on NEW DRUG DISCOVERY AND DEVELOPMENT STAGES OF DRUG DISCOVERYNEHA GUPTA
The process of drug discovery and development is a complex and multi-step endeavor aimed at bringing new pharmaceutical drugs to market. It begins with identifying and validating a biological target, such as a protein, gene, or RNA, that is associated with a disease. This step involves understanding the target's role in the disease and confirming that modulating it can have therapeutic effects. The next stage, hit identification, employs high-throughput screening (HTS) and other methods to find compounds that interact with the target. Computational techniques may also be used to identify potential hits from large compound libraries.
Following hit identification, the hits are optimized to improve their efficacy, selectivity, and pharmacokinetic properties, resulting in lead compounds. These leads undergo further refinement to enhance their potency, reduce toxicity, and improve drug-like characteristics, creating drug candidates suitable for preclinical testing. In the preclinical development phase, drug candidates are tested in vitro (in cell cultures) and in vivo (in animal models) to evaluate their safety, efficacy, pharmacokinetics, and pharmacodynamics. Toxicology studies are conducted to assess potential risks.
Before clinical trials can begin, an Investigational New Drug (IND) application must be submitted to regulatory authorities. This application includes data from preclinical studies and plans for clinical trials. Clinical development involves human trials in three phases: Phase I tests the drug's safety and dosage in a small group of healthy volunteers, Phase II assesses the drug's efficacy and side effects in a larger group of patients with the target disease, and Phase III confirms the drug's efficacy and monitors adverse reactions in a large population, often compared to existing treatments.
After successful clinical trials, a New Drug Application (NDA) is submitted to regulatory authorities for approval, including all data from preclinical and clinical studies, as well as proposed labeling and manufacturing information. Regulatory authorities then review the NDA to ensure the drug is safe, effective, and of high quality, potentially requiring additional studies. Finally, after a drug is approved and marketed, it undergoes post-marketing surveillance, which includes continuous monitoring for long-term safety and effectiveness, pharmacovigilance, and reporting of any adverse effects.
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Antimicrobial stewardship to prevent antimicrobial resistanceGovindRankawat1
India is among the nations with the highest burden of bacterial infections.
India is one of the largest consumers of antibiotics worldwide.
India carries one of the largest burdens of drug‑resistant pathogens worldwide.
Highest burden of multidrug‑resistant tuberculosis,
Alarmingly high resistance among Gram‑negative and Gram‑positive bacteria even to newer antimicrobials such as carbapenems.
NDM‑1 ( New Delhi Metallo Beta lactamase 1, an enzyme which inactivates majority of Beta lactam antibiotics including carbapenems) was reported in 2008
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
36. Distributed
Learning
Architecture Update Model
Learn Model
from Local Data
Central Server
Model Server
RTOG
Send Model
Parameters
Final Model Created
Learn Model
from Local Data
Learn Model
from Local Data
Model Server
MAASTRO
Model Server
Roma
Send Model
Parameters
Send Model
Parameters
Send Average
Consensus
Model
Send Average
Consensus
Model
Send Average
Consensus
Model
Only aggregate data is exchanged between the Central Server and the local Servers
I try to have hobbies…Mechatronics : a lot of INGS, ICS and TICS
Info / prog man : privilege of having my share in managing Medical Physics Engineers & Data Analysts
MAASTRICHT: capitol of province South Limburg (122.000 inhabitants)Habitat of MAASTRO clinic (Maastricht Radiation Oncology)Lead Inspirational Professor Philippe LambinPay close attention to one thing!No, not the once-in-a-lifetime clean desk…The Personalized Treatment Decision Support prototype
Decision Support Systems as foundation of our organizationWhy is this needed?
From population-based to personal healthcare.However, population data needed for individual decision makingSlide taken from ORACLE presentatoin:The intersection of the 2 industries starts with PV on LS side and Safety at Point of Care on HC side. So it is not the end result, it is first and most active step in move toward personalized health.Future & Oracle vision: multiple data sources from LS & HC co-exist and one can apply all the traditional reactive engines and predictive event-based engines for real-time information on impact of the product. Feed knowledge back into drug development lifecycle mgmt. Patient safety is immediate benefit, l/t benefit is better understanding of patient population.