The document discusses the objectives, motivation, methodology, and design of an Artificial Intelligence Diagnosis (AID) system. The objectives are to: [1] assist medical professionals in diagnosis; [2] predict probable diseases and diagnoses; and [3] provide personalized healthcare to patients. The motivation is addressing issues like limited doctors, overlooked details, and reliance on subjective diagnosis. The methodology extracts rules from medical data and literature to generate diagnoses, ranks possible diseases, and stores past diagnoses to improve accuracy over time. The system design integrates these components to build an expert system that aims to better serve patients and the medical community.
Simvastatin in Aneurysmal Subarachnoid Hemorrhage (SASH) Trial is a prospective, randomized, double-blind, placebo-controlled pilot trial that assessed the role of simvastatin in preventing vasospasm and improving outcomes in patients with aneurysmal subarachnoid hemorrhage. The trial randomized 38 patients to receive either simvastatin 80mg or placebo for 14 days after aneurysm clipping. Results showed lower rates of vasospasm, neurological deterioration and mortality in the simvastatin group, though differences were not statistically significant. Larger multicenter trials are still needed to definitively determine if statins provide clinical benefits for aneurysmal subarachnoid hemorrhage
SIMVASTATIN IN ANEURYSMAL SUBARACHNOID HEAMORRHAGE (SASH) TRIAL Sumit2018
This document describes the SASH trial, a prospective randomized double-blind placebo-controlled pilot study that assessed the role of simvastatin in preventing vasospasm and improving outcomes in patients with aneurysmal subarachnoid hemorrhage (SAH). The study found lower rates of vasospasm, neurological deterioration, and mortality in the simvastatin group compared to placebo, though the differences were not statistically significant due to the small sample size. The document concludes that while statins may provide benefits, larger phase III studies are still needed to definitively determine if statins improve outcomes for SAH patients.
This document describes using machine learning techniques to detect heart disease. It discusses applying data analytics methods like SVM and genetic algorithms to large datasets to better predict, prevent, and manage cardiovascular diseases. The proposed system aims to use these machine learning methods to predict heart attacks and reduce treatment costs by providing effective treatments. It evaluates models based on accuracy, elapsed time, and energy consumption to determine the best optimized prediction model.
The document discusses evolving consensus-based curation strategies for the Guide to PHARMACOLOGY database. It summarizes how the database overlays data from multiple sources to define consensus lists of approved drugs and their targets. Through comparing various sources, the database curators established consensus sets of 202 drug targets and 923 approved drugs. The curators aim to balance comprehensive coverage with pragmatic utility by focusing on data-supported relationships between drugs, targets, and activities.
Prof. Todor (Ted) A. Popov - 6th Clinical Research ConferenceStarttech Ventures
Ομιλία - Παρουσίαση: Prof. Todor (Ted) A. Popov, Professor of Medicine, Medical University in Sofia, Chairman of the Bulgarian Ethics Committee for Multicenter Studies
Τίτλος Παρουσίασης: «Do databases around the world speak the same language?»
AI for Precision Medicine (Pragmatic preclinical data science)Paul Agapow
This document summarizes a presentation on using data science approaches like machine learning for precision medicine and biomedical research. It notes that biomedical data sets are often small, which limits the use of deep learning techniques that require large amounts of labeled data. It advocates combining multiple smaller datasets together using standards to create larger datasets for analysis. It also emphasizes using multiple data types (e.g. omics data, electronic health records, social media) together through integrated analysis to provide more context than any single data type alone. It provides examples of applying these approaches to problems like classifying texts for systematic reviews and discovering asthma subtypes through multi-omics analysis.
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
The document discusses the objectives, motivation, methodology, and design of an Artificial Intelligence Diagnosis (AID) system. The objectives are to: [1] assist medical professionals in diagnosis; [2] predict probable diseases and diagnoses; and [3] provide personalized healthcare to patients. The motivation is addressing issues like limited doctors, overlooked details, and reliance on subjective diagnosis. The methodology extracts rules from medical data and literature to generate diagnoses, ranks possible diseases, and stores past diagnoses to improve accuracy over time. The system design integrates these components to build an expert system that aims to better serve patients and the medical community.
Simvastatin in Aneurysmal Subarachnoid Hemorrhage (SASH) Trial is a prospective, randomized, double-blind, placebo-controlled pilot trial that assessed the role of simvastatin in preventing vasospasm and improving outcomes in patients with aneurysmal subarachnoid hemorrhage. The trial randomized 38 patients to receive either simvastatin 80mg or placebo for 14 days after aneurysm clipping. Results showed lower rates of vasospasm, neurological deterioration and mortality in the simvastatin group, though differences were not statistically significant. Larger multicenter trials are still needed to definitively determine if statins provide clinical benefits for aneurysmal subarachnoid hemorrhage
SIMVASTATIN IN ANEURYSMAL SUBARACHNOID HEAMORRHAGE (SASH) TRIAL Sumit2018
This document describes the SASH trial, a prospective randomized double-blind placebo-controlled pilot study that assessed the role of simvastatin in preventing vasospasm and improving outcomes in patients with aneurysmal subarachnoid hemorrhage (SAH). The study found lower rates of vasospasm, neurological deterioration, and mortality in the simvastatin group compared to placebo, though the differences were not statistically significant due to the small sample size. The document concludes that while statins may provide benefits, larger phase III studies are still needed to definitively determine if statins improve outcomes for SAH patients.
This document describes using machine learning techniques to detect heart disease. It discusses applying data analytics methods like SVM and genetic algorithms to large datasets to better predict, prevent, and manage cardiovascular diseases. The proposed system aims to use these machine learning methods to predict heart attacks and reduce treatment costs by providing effective treatments. It evaluates models based on accuracy, elapsed time, and energy consumption to determine the best optimized prediction model.
The document discusses evolving consensus-based curation strategies for the Guide to PHARMACOLOGY database. It summarizes how the database overlays data from multiple sources to define consensus lists of approved drugs and their targets. Through comparing various sources, the database curators established consensus sets of 202 drug targets and 923 approved drugs. The curators aim to balance comprehensive coverage with pragmatic utility by focusing on data-supported relationships between drugs, targets, and activities.
Prof. Todor (Ted) A. Popov - 6th Clinical Research ConferenceStarttech Ventures
Ομιλία - Παρουσίαση: Prof. Todor (Ted) A. Popov, Professor of Medicine, Medical University in Sofia, Chairman of the Bulgarian Ethics Committee for Multicenter Studies
Τίτλος Παρουσίασης: «Do databases around the world speak the same language?»
AI for Precision Medicine (Pragmatic preclinical data science)Paul Agapow
This document summarizes a presentation on using data science approaches like machine learning for precision medicine and biomedical research. It notes that biomedical data sets are often small, which limits the use of deep learning techniques that require large amounts of labeled data. It advocates combining multiple smaller datasets together using standards to create larger datasets for analysis. It also emphasizes using multiple data types (e.g. omics data, electronic health records, social media) together through integrated analysis to provide more context than any single data type alone. It provides examples of applying these approaches to problems like classifying texts for systematic reviews and discovering asthma subtypes through multi-omics analysis.
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
This document discusses using machine learning algorithms to detect heart disease. It proposes using support vector machines (SVM) and genetic algorithms to analyze large datasets to better predict, prevent, and manage cardiovascular diseases. The existing systems have high treatment costs and low accuracy. The proposed system aims to more accurately predict heart attacks using data preprocessing and SVM/genetic algorithm models to reduce costs and increase effectiveness. It details the system requirements, modules, dataflow, and provides screenshots of a sample interface.
Hani Tamim is a professor of epidemiology and biostatistics at Al-Faisal University in Riyadh, Saudi Arabia. Biostatistics involves applying statistical methods to answer biological and health-related research questions, such as determining the survival rate among ICU patients or the incidence of Down's syndrome in a population. The research process involves formulating a research question, reviewing literature, designing a study, collecting and analyzing data, and reporting results. Statistics is used to organize, summarize, and make inferences about data to address research questions.
NLP (Natural Language Processing) shows a great deal of potential for many applications in the healthcare industry. This document shares 6 promising use cases for NLP to manage Epilepsy treatment effectively.
This document discusses several common problems with data handling and quality including building and testing models with the same data, confusion between biological and technical replicates, and identification and handling of outliers. It provides examples and explanations of key concepts such as experimental and sampling units, pseudo-replication, outliers versus high influence points, and leverage plots. The importance of proper data handling techniques like dividing data into training, test, and confirmation sets and using cross-validation is emphasized to avoid overfitting models and generating spurious findings.
Introduction to MedDRA Coding in Drug Safety & Pharmacovigilance Process for Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
1Big Data Analytics forHealthcareChandan K. ReddyD.docxaulasnilda
1
Big Data Analytics for
Healthcare
Chandan K. Reddy
Department of Computer Science
Wayne State University
Jimeng Sun
Healthcare Analytics Department
IBM TJ Watson Research Center
2Jimeng Sun, Large-scale Healthcare Analytics
Healthcare Analytics using Electronic Health Records (EHR)
Old way: Data are expensive and small
– Input data are from clinical trials, which is small
and costly
– Modeling effort is small since the data is limited
• A single model can still take months
EHR era: Data are cheap and large
– Broader patient population
– Noisy data
– Heterogeneous data
– Diverse scale
– Complex use cases
3Jimeng Sun, Large-scale Healthcare Analytics
Heterogeneous Medical Data
DiagnosisDiagnosis
MedicationMedication
LabLab
Clinical
notes
Clinical
notes
ImagesImages
Genetic
data
Genetic
data
4Jimeng Sun, Large-scale Healthcare Analytics
Challenges of Healthcare AnalyticsScalability ChallengesChallenges in Healthcare Analytics
Collaboration across domains
Analytic platform
Intuitive results
Scalable computation
5
PARALLEL MODEL BUILDING
6Jimeng Sun, Large-scale Healthcare Analytics
Motivation – Predictive modeling using EHR is growing
Need for scalable predictive modeling platforms/systems due to increased
computational requirements from:
– Processing EHR data (due to volume, variability, and heterogeneity)
– Building accurate models
– Building clinically meaningful models
– Validating models for accuracy and generalizability
Explosion in
interest
7Jimeng Sun, Large-scale Healthcare Analytics
What does it take to develop a predictive model using EHR?
Marina: IBM
Analytics Consultant
1
2
3
4
5
Within 3 months, we need to
1. understand business case
2. obtain the data
3. prepare the data
4. develop predictive models
5. deliver the final model
David Gotz, Harry Starvropoulos, Jimeng Sun, Fei Wang.
ICDA: A Platform for Intelligent Care Delivery Analytics, AMIA 2012
8Jimeng Sun, Large-scale Healthcare Analytics
A Generalized Predictive Modeling Pipeline
Cohort Construction: Find an appropriate set of patients with the specified
target condition and a corresponding set of control patients without the
condition.
Feature Construction: Compute a feature vector representation for each
patient based on the patient’s EHR data.
Cross Validation: Partition the data into complementary subsets for use in
model training and validation testing.
Feature Selection: Rank the input features and select a subset of relevant
features for use in the model.
Classification: The training and evaluation of a model for a specific classifier.
Output: Clean up intermediate files and to put results into their final locations.
Model specification
9Jimeng Sun, Large-scale Healthcare Analytics
Cohort Construction
A
ll
pa
tie
nt
s
D1
Disease Target samples
D1 Hypertension control 5000
D2 Heart failure onset 33K
D3 Hypertension diagnosis 300K
Cases
Controls
D3
D2
10Jimeng Sun, Large- ...
Clinical Validation of Copy Number Variants Using the AMP GuidelinesGolden Helix
This document discusses Golden Helix's software for clinical variant analysis and summarizing copy number variants using American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP) guidelines. It acknowledges funding support from several National Institutes of Health grants. It also lists upcoming discussion sessions on applying ACMG/AMP guidelines in clinical practice and analyzing copy number variants from next-generation sequencing data.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
BioGears Overview for SSIH Healthcare Systems Modeling & Simulation Affinity ...BioGearsEngine
Our Principal Investigator, Austin Baird, provides an overview of the BioGears Human Physiology Engine and explains several use cases for physiology modeling and simulation.
This document provides a summary of a project to analyze factors related to readmission of diabetes patients using a dataset from 130 US hospitals. The team cleaned the data by removing attributes with high percentages of missing values, irrelevant attributes, and instances of deceased patients. They applied the SMOTE technique to address data imbalance, oversampling the minority readmission class by 200%. Three classifiers - J48 decision tree, Naive Bayes, and Bayes Net - were selected for experiments to predict patient readmission.
Disease phenotypes are descriptions of clinically observable or measurable traits that characterize a target disease and its associated patient cohort of interest (e.g., using HbA1C measurements, medical codes and other criteria to identify patients with type II diabetes). As health data become increasingly digitalized through use of electronic health records (EHR), data-driven phenotyping has been developed as a new discipline that aims to quickly identify disease-specific cohorts from large datasets and gain insights into disease dynamics through ever-changing real-world evidence. In this context, the word "phenotype" effectively takes on new semantics as "computable phenotype," which generally refers to any clinical patterns inferred from EHR (and often genomic data as well) that can be used to make assertions about patients and their clinical conditions.
EHRs, however, are noisy practice-based patient data, collected primarily for healthcare delivery and therefore present a great representational gap to biomedical research like disease phenotyping. As a result, most of the computational phenotyping methods today are predominantly rule-based: Clinical experts pre-specify based on their domain knowledge – a set of phenotyping rules in terms of narrative descriptions, logical expressions and workflows – that captures the pathology and relevant medical observations of a disease cohort.
Rule-based methods often involve a long development cycle subject to site-dependent interpretations (of the phenotyping algorithm), knowledge engineering and programming exercises that can often stretch beyond several months to accomplish phenotyping merely a single disease. To achieve a better scalability while enabling generalization for more complex diseases in the phenotyping process -- where phenotype definitions are unclear but relevant medical concepts/patterns can be learned statistically from large-scale EHR data -- a large portion of my recent work in this area has been focusing on developing automated, statistical machine learning-based phenotyping methodologies.
In these slides, I will present an overview of health data-driven disease phenotyping with a focus on one example project – bulk learning – an EHR-based, multi-disease phenotyping framework.
In essence, bulk learning uses a hierarchical learning approach, combined with medical ontology, to derive diagnostic components that collectively serve as phenotyping rules for a group of infectious diseases. As a multiple-disease phenotyping framework, it works in a similar fashion to medical diagnosis settings (albeit through statistical means) where relevant medical concepts such as microbiology lab tests, intravenous chemistry tests, among others, are used as supporting evidence with varying degrees of confidence, estimated statistically from data, for determining positive cases while ruling out the negatives with probabilities.
A Value-Based Approach to Clinical Pathology and InformaticsCirdan
A presentation delivered by Dr. Glenn Edwards, SA Pathology at the Pathology Horizons 2017 conference in Cairns, Australia.
Pathology Horizons is an annual CPD conference organised by Cirdan on the future of pathology. More information on Pathology Horizons can be accessed at www.pathologyhorizons.com
Natural Language Processing to Curate Unstructured Electronic Health RecordsMMS Holdings
This presentation provides an overview of Natural Language Processing (NLP), an Artificial Intelligence technique that can be used to curate unstructured medical records. We will see NLP in action as part of the ICODA Grand Challenges ‘PRIEST’ project (Pandemic Respiratory Infection Emergency System Triage) Study for Low and Middle-Income Countries as a case study.
Watch full webinar -
https://info.mmsholdings.com/natural-language-processing-webinar-july-2022
Data in genomics: Dr Richard Scott, Clinical Lead for Rare Disease, 100,000 G...NHS England
This document discusses the importance of structured data and standardized nomenclature for analyzing genomic data at scale. It notes that every person's genome contains 3 billion DNA base pairs with around 5 million variants compared to the reference genome. The 100,000 Genomes Project aims to generate genomic and clinical data from 100,000 participants to help find treatments for rare diseases. Key challenges discussed include dealing with large amounts of genomic and associated unstructured clinical data, and the need for automated and standardized approaches using structured data models and established clinical terminologies to enable machine learning and clinical interpretation of the data.
Metabolic Profiling_techniques and approaches.pptSachin Teotia
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in areas like instrumentation variability, data treatment, identification, and lack of standardization. Their work seeks to advance the field and provide insights into biochemistry, biomarkers, disease, and treatment responses through holistic analysis of small molecules.
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in analytical procedures, data treatment, and lack of standardization. Their work seeks to advance metabolomics as an expanding field that provides insights into biochemistry and discovers biomarkers for health and disease.
1. The document discusses using machine learning techniques to predict heart disease by evaluating large datasets to identify patterns that can help predict, prevent, and manage conditions like heart attacks.
2. It proposes using data analytics based on support vector machines and genetic algorithms to diagnose heart disease, claiming genetic algorithms provide the best optimized prediction models.
3. The key modules described are uploading training data, pre-processing the heart disease data, using machine learning to predict heart disease, and generating graphical representations of the analyses.
Microfluidic Flow Control using Magnetohydrodynamics KayDrive
Fluid manipulation in microfluidic devices is one of the main areas of research interest for the fabrication of Lab-On-a-Chip devices. From the many methods that have been applied to this problem, one of the most promising is employing Magnetohydrodynamic principles which allow for elegant and versatile designs. A microchip is designed for fluid flow control that uses MHD for pumping the fluid through a microchannel. Simulation of the design is performed in COMSOL and the velocity profile of the fluid is obtained. The microchip is fabricated, and experiments are performed by measuring the flow rate of a conducting fluid as it is pumped by the Lorentz force. The experimental results are then compared with the simulation results to compare the performance of the device to theoretical computations.
The main objective of this project is to advance the local merchants, by creating a community where people can share their meaningful experiences and help each other find the best option available out there, saving time and money.
This document discusses using machine learning algorithms to detect heart disease. It proposes using support vector machines (SVM) and genetic algorithms to analyze large datasets to better predict, prevent, and manage cardiovascular diseases. The existing systems have high treatment costs and low accuracy. The proposed system aims to more accurately predict heart attacks using data preprocessing and SVM/genetic algorithm models to reduce costs and increase effectiveness. It details the system requirements, modules, dataflow, and provides screenshots of a sample interface.
Hani Tamim is a professor of epidemiology and biostatistics at Al-Faisal University in Riyadh, Saudi Arabia. Biostatistics involves applying statistical methods to answer biological and health-related research questions, such as determining the survival rate among ICU patients or the incidence of Down's syndrome in a population. The research process involves formulating a research question, reviewing literature, designing a study, collecting and analyzing data, and reporting results. Statistics is used to organize, summarize, and make inferences about data to address research questions.
NLP (Natural Language Processing) shows a great deal of potential for many applications in the healthcare industry. This document shares 6 promising use cases for NLP to manage Epilepsy treatment effectively.
This document discusses several common problems with data handling and quality including building and testing models with the same data, confusion between biological and technical replicates, and identification and handling of outliers. It provides examples and explanations of key concepts such as experimental and sampling units, pseudo-replication, outliers versus high influence points, and leverage plots. The importance of proper data handling techniques like dividing data into training, test, and confirmation sets and using cross-validation is emphasized to avoid overfitting models and generating spurious findings.
Introduction to MedDRA Coding in Drug Safety & Pharmacovigilance Process for Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
1Big Data Analytics forHealthcareChandan K. ReddyD.docxaulasnilda
1
Big Data Analytics for
Healthcare
Chandan K. Reddy
Department of Computer Science
Wayne State University
Jimeng Sun
Healthcare Analytics Department
IBM TJ Watson Research Center
2Jimeng Sun, Large-scale Healthcare Analytics
Healthcare Analytics using Electronic Health Records (EHR)
Old way: Data are expensive and small
– Input data are from clinical trials, which is small
and costly
– Modeling effort is small since the data is limited
• A single model can still take months
EHR era: Data are cheap and large
– Broader patient population
– Noisy data
– Heterogeneous data
– Diverse scale
– Complex use cases
3Jimeng Sun, Large-scale Healthcare Analytics
Heterogeneous Medical Data
DiagnosisDiagnosis
MedicationMedication
LabLab
Clinical
notes
Clinical
notes
ImagesImages
Genetic
data
Genetic
data
4Jimeng Sun, Large-scale Healthcare Analytics
Challenges of Healthcare AnalyticsScalability ChallengesChallenges in Healthcare Analytics
Collaboration across domains
Analytic platform
Intuitive results
Scalable computation
5
PARALLEL MODEL BUILDING
6Jimeng Sun, Large-scale Healthcare Analytics
Motivation – Predictive modeling using EHR is growing
Need for scalable predictive modeling platforms/systems due to increased
computational requirements from:
– Processing EHR data (due to volume, variability, and heterogeneity)
– Building accurate models
– Building clinically meaningful models
– Validating models for accuracy and generalizability
Explosion in
interest
7Jimeng Sun, Large-scale Healthcare Analytics
What does it take to develop a predictive model using EHR?
Marina: IBM
Analytics Consultant
1
2
3
4
5
Within 3 months, we need to
1. understand business case
2. obtain the data
3. prepare the data
4. develop predictive models
5. deliver the final model
David Gotz, Harry Starvropoulos, Jimeng Sun, Fei Wang.
ICDA: A Platform for Intelligent Care Delivery Analytics, AMIA 2012
8Jimeng Sun, Large-scale Healthcare Analytics
A Generalized Predictive Modeling Pipeline
Cohort Construction: Find an appropriate set of patients with the specified
target condition and a corresponding set of control patients without the
condition.
Feature Construction: Compute a feature vector representation for each
patient based on the patient’s EHR data.
Cross Validation: Partition the data into complementary subsets for use in
model training and validation testing.
Feature Selection: Rank the input features and select a subset of relevant
features for use in the model.
Classification: The training and evaluation of a model for a specific classifier.
Output: Clean up intermediate files and to put results into their final locations.
Model specification
9Jimeng Sun, Large-scale Healthcare Analytics
Cohort Construction
A
ll
pa
tie
nt
s
D1
Disease Target samples
D1 Hypertension control 5000
D2 Heart failure onset 33K
D3 Hypertension diagnosis 300K
Cases
Controls
D3
D2
10Jimeng Sun, Large- ...
Clinical Validation of Copy Number Variants Using the AMP GuidelinesGolden Helix
This document discusses Golden Helix's software for clinical variant analysis and summarizing copy number variants using American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP) guidelines. It acknowledges funding support from several National Institutes of Health grants. It also lists upcoming discussion sessions on applying ACMG/AMP guidelines in clinical practice and analyzing copy number variants from next-generation sequencing data.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
BioGears Overview for SSIH Healthcare Systems Modeling & Simulation Affinity ...BioGearsEngine
Our Principal Investigator, Austin Baird, provides an overview of the BioGears Human Physiology Engine and explains several use cases for physiology modeling and simulation.
This document provides a summary of a project to analyze factors related to readmission of diabetes patients using a dataset from 130 US hospitals. The team cleaned the data by removing attributes with high percentages of missing values, irrelevant attributes, and instances of deceased patients. They applied the SMOTE technique to address data imbalance, oversampling the minority readmission class by 200%. Three classifiers - J48 decision tree, Naive Bayes, and Bayes Net - were selected for experiments to predict patient readmission.
Disease phenotypes are descriptions of clinically observable or measurable traits that characterize a target disease and its associated patient cohort of interest (e.g., using HbA1C measurements, medical codes and other criteria to identify patients with type II diabetes). As health data become increasingly digitalized through use of electronic health records (EHR), data-driven phenotyping has been developed as a new discipline that aims to quickly identify disease-specific cohorts from large datasets and gain insights into disease dynamics through ever-changing real-world evidence. In this context, the word "phenotype" effectively takes on new semantics as "computable phenotype," which generally refers to any clinical patterns inferred from EHR (and often genomic data as well) that can be used to make assertions about patients and their clinical conditions.
EHRs, however, are noisy practice-based patient data, collected primarily for healthcare delivery and therefore present a great representational gap to biomedical research like disease phenotyping. As a result, most of the computational phenotyping methods today are predominantly rule-based: Clinical experts pre-specify based on their domain knowledge – a set of phenotyping rules in terms of narrative descriptions, logical expressions and workflows – that captures the pathology and relevant medical observations of a disease cohort.
Rule-based methods often involve a long development cycle subject to site-dependent interpretations (of the phenotyping algorithm), knowledge engineering and programming exercises that can often stretch beyond several months to accomplish phenotyping merely a single disease. To achieve a better scalability while enabling generalization for more complex diseases in the phenotyping process -- where phenotype definitions are unclear but relevant medical concepts/patterns can be learned statistically from large-scale EHR data -- a large portion of my recent work in this area has been focusing on developing automated, statistical machine learning-based phenotyping methodologies.
In these slides, I will present an overview of health data-driven disease phenotyping with a focus on one example project – bulk learning – an EHR-based, multi-disease phenotyping framework.
In essence, bulk learning uses a hierarchical learning approach, combined with medical ontology, to derive diagnostic components that collectively serve as phenotyping rules for a group of infectious diseases. As a multiple-disease phenotyping framework, it works in a similar fashion to medical diagnosis settings (albeit through statistical means) where relevant medical concepts such as microbiology lab tests, intravenous chemistry tests, among others, are used as supporting evidence with varying degrees of confidence, estimated statistically from data, for determining positive cases while ruling out the negatives with probabilities.
A Value-Based Approach to Clinical Pathology and InformaticsCirdan
A presentation delivered by Dr. Glenn Edwards, SA Pathology at the Pathology Horizons 2017 conference in Cairns, Australia.
Pathology Horizons is an annual CPD conference organised by Cirdan on the future of pathology. More information on Pathology Horizons can be accessed at www.pathologyhorizons.com
Natural Language Processing to Curate Unstructured Electronic Health RecordsMMS Holdings
This presentation provides an overview of Natural Language Processing (NLP), an Artificial Intelligence technique that can be used to curate unstructured medical records. We will see NLP in action as part of the ICODA Grand Challenges ‘PRIEST’ project (Pandemic Respiratory Infection Emergency System Triage) Study for Low and Middle-Income Countries as a case study.
Watch full webinar -
https://info.mmsholdings.com/natural-language-processing-webinar-july-2022
Data in genomics: Dr Richard Scott, Clinical Lead for Rare Disease, 100,000 G...NHS England
This document discusses the importance of structured data and standardized nomenclature for analyzing genomic data at scale. It notes that every person's genome contains 3 billion DNA base pairs with around 5 million variants compared to the reference genome. The 100,000 Genomes Project aims to generate genomic and clinical data from 100,000 participants to help find treatments for rare diseases. Key challenges discussed include dealing with large amounts of genomic and associated unstructured clinical data, and the need for automated and standardized approaches using structured data models and established clinical terminologies to enable machine learning and clinical interpretation of the data.
Metabolic Profiling_techniques and approaches.pptSachin Teotia
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in areas like instrumentation variability, data treatment, identification, and lack of standardization. Their work seeks to advance the field and provide insights into biochemistry, biomarkers, disease, and treatment responses through holistic analysis of small molecules.
This document discusses metabolomics profiling and the challenges faced by analytical chemists. It outlines the group's work at Aristotle University on developing new analytical methods, standardizing data extraction and quality control protocols, identifying metabolites, and collaborating across disciplines. The group aims to address bottlenecks in analytical procedures, data treatment, and lack of standardization. Their work seeks to advance metabolomics as an expanding field that provides insights into biochemistry and discovers biomarkers for health and disease.
1. The document discusses using machine learning techniques to predict heart disease by evaluating large datasets to identify patterns that can help predict, prevent, and manage conditions like heart attacks.
2. It proposes using data analytics based on support vector machines and genetic algorithms to diagnose heart disease, claiming genetic algorithms provide the best optimized prediction models.
3. The key modules described are uploading training data, pre-processing the heart disease data, using machine learning to predict heart disease, and generating graphical representations of the analyses.
Microfluidic Flow Control using Magnetohydrodynamics KayDrive
Fluid manipulation in microfluidic devices is one of the main areas of research interest for the fabrication of Lab-On-a-Chip devices. From the many methods that have been applied to this problem, one of the most promising is employing Magnetohydrodynamic principles which allow for elegant and versatile designs. A microchip is designed for fluid flow control that uses MHD for pumping the fluid through a microchannel. Simulation of the design is performed in COMSOL and the velocity profile of the fluid is obtained. The microchip is fabricated, and experiments are performed by measuring the flow rate of a conducting fluid as it is pumped by the Lorentz force. The experimental results are then compared with the simulation results to compare the performance of the device to theoretical computations.
The main objective of this project is to advance the local merchants, by creating a community where people can share their meaningful experiences and help each other find the best option available out there, saving time and money.
The gist of our project encapsulates mainly the manufacturing phase of a ring spinning machine.
A ring spinning machine forms threads by isolating individual fibers from yarn, and twisting those fibers co-axially about each other, hence forming a single coalescent thread having been imparted the strength of each fiber.
This project was designed and manufactured locally and the cost was reduced by 78.33% by comparing it with imported machinery.
This document outlines a senior design project for an energy audit and cost analysis of an ammonia plant. The objectives are to understand what an energy audit is, why companies need them, and to analyze the energy efficiency and bottlenecks in Fatima Fertilizers' process. The team will collect plant data, perform calculations and simulations, analyze results, recommend improvements, and conduct a cost analysis. The timeline shows milestones from November 2020 to May 2021. Software like Excel, Polymath, AspenHysys, and MATLAB will be used, along with energy index data from Fatima. Regular communication with industry professionals is also part of the project resources and references.
CodeX is a big data analytics solution that uses novel secure processing and highly efficient data search and analysis techniques. It has a microservices-based architecture in the cloud that offers unique services to Fortune 500 customers. Pegasus is a highly distributed big data storage and ETL solution that can handle structured, unstructured and semi-structured data within the same architecture using different databases. It is optimized for open source intelligence data and integrates with CodeX's big data platform.
VTrack is a mobile app that allows schools and parents to track student transportation vans. It provides live tracking of each van's location, speed, and route. Parents receive notifications of their child's pick-up and drop-off times. They can also provide feedback to the school. The app aims to ease parents' safety concerns by increasing visibility into their child's transportation. It was originally intended for a university service but shifted focus to grade schools where tracking young kids is most useful.
HearAct is a sign language interpreter for Pakistani Sign Language (PSL) that uses sensors to record hand orientation and gesture coordinates and passes that data to a recognition model to display the corresponding word or sentence. It aims to help the over 250,000 deaf or speech-impaired people in Pakistan communicate by reducing their dependency on others and enabling mobility through a portable, cost-effective solution. The project is supervised by Sir Abdul Basit and developed by team members Ayesha Dojky, Saif Rehman, Shehrbanu Karim, and Zahra Hussaini.
Woxcut is a platform that allows users to create and run cryptocurrency trading bots with custom strategies. It aims to allow users to choose from pre-existing strategies, integrate with existing exchanges via APIs, and run bot instances on the cloud. The platform also seeks to develop an internal machine learning model to help users make optimal trading decisions.
The document describes a project called "Colour It" that aims to automatically colorize grayscale images without human assistance. The system trains a computational neural network on over a million colored images to learn statistical dependencies between image semantics and textures and their colored versions. Users can upload grayscale images to be colorized by the system in a fast and realistic way. The goals are to give users the ability to easily colorize images with minimal processing time and output images that are close to the original ground truths. This technique could benefit medical imaging and colorizing old black and white films while training convolutional neural networks.
GoSpark is a platform that uses beacons and a mobile application to track customer behavior in stores. This allows businesses to gain insights into shopping patterns and trends. The goal is to help retailers increase sales and customer loyalty by providing personalized promotions and a better understanding of customers. The project will require implementing a website and mobile app connected to beacons placed in stores to anonymously track customer movement and send targeted notifications.
1. Beautyou is a virtual makeup applicator that allows users to try on makeup, lenses, and accessories virtually before purchasing through an e-commerce website.
2. The objectives are to build a virtual makeup trier to help users safely apply products virtually and promote online purchases of makeup.
3. Motivations include the growth of the beauty industry, need for solutions to virtually try products at home, and improvements in augmented reality technology.
The document describes a project called "Colour It" that aims to automatically colorize grayscale images without human assistance. The system trains a computational neural network on over a million colored images to learn statistical dependencies between image semantics and textures and their colored versions. Users can upload grayscale images to be colorized by the system in a fast and realistic way. The goals are to give users the ability to easily colorize images with minimal processing time and for the colored image to closely match the original if it was in color. This technique could benefit areas like medical imaging and restoring old black and white films and videos.
The document describes a mobile application called Nan-Baby that connects parents with babysitters. The application was created by students Syeda Ayesha Fahim Junaid Zia Khan Kehkashan Salman and is supervised by Mr. Asad Ali. Nan-Baby aims to provide qualified, educated babysitters to watch children either at the babysitter's home or the parent's home. The motivation was to create opportunities for female students in Pakistan to babysit. The app allows parents to book babysitters near them and choose between having the babysitter come to them or dropping the child off.
Shift is a mobile app that uses image recognition to help online shoppers find products. It allows users to upload photos of items they want to find and it will identify colors, shapes, sizes and product categories to provide matching results. This provides a better search experience than relying only on keywords, as Shift can identify hard-to-describe items. The app aims to reduce average search times for customers and provide a simpler way to find products online through its augmented reality search features.
The document proposes a spatial design solution for social and educational reformation for street children in Karachi. The design includes a master plan with blocks for vocational training, detoxification, rehabilitation and administration, residence, and views. The plan aims to provide street children services for detoxification, rehabilitation, vocational training, residence, and administration.
This document outlines a final year project to develop a scheduling algorithm using genetic algorithms. The project is supervised by Dr. Imran Khan and involves Bushra Qureshi, Maham Faiz, Mariam Imran, and Sheeza Shakeel. The algorithm aims to effectively schedule classes while addressing constraints like classroom availability, professor and student schedules, and course requirements. It will use genetic algorithm operators like initialization, selection, crossover and mutation to generate timetables that satisfy constraints and optimize resource allocation and scheduling. The resulting automated timetable generation process is expected to reduce time spent creating schedules and address issues like clashes in current manual systems.
This document outlines a final year project to design a UAV that can transform into a UGV for 3D mapping and image processing. It includes sections on the overview, block diagram, methodology, flow chart, Gantt chart, and conclusion. The objectives are to create a low-cost hybrid quadcopter that can fly autonomously using sensors and also navigate on the ground for applications like search and rescue, disaster response, and surveillance missions. It proposes using a rolling cage mechanism for the UAV to UGV transformation.
This document discusses using virtual reality exposure therapy with a Kinect motion sensor to treat phobias at medical rehabilitation centers in Pakistan. It proposes developing virtual reality environments that systematically expose patients to phobia triggers, such as heights for acrophobia therapy. Market research found interest from private hospitals to use the tool for treating anxiety disorders. The revenue model would involve subscription or software/hardware packages, and software maintenance costs. Key milestones achieved include designing interfaces for acrophobia therapy with increasing exposure levels and developing early environments for claustrophobia and spider phobia therapy.
Marketmizer is a software that will optimize Jovago Pakistan's marketing budget allocation across different online channels like SEM, social media, affiliates etc. based on the company's past marketing performance data. It will help address issues with Jovago's current manual budgeting approach by predicting future bookings and providing visualizations of marketing trends. The software will utilize machine learning algorithms and genetic algorithms to model marketing data, identify trends, validate predictions and suggest an optimal budget allocation to help Jovago achieve targets like reducing costs per booking while increasing revenues and lowering bounce rates.
CodeX is a big data analytics company that offers unique services to Fortune 500 customers through its microservices-based cloud platform. Pegasus is CodeX's big data ETL solution that handles structured, unstructured, and semi-structured data within the same distributed storage architecture using different databases for different data types. Pegasus also has built-in ETL capabilities, allows querying across databases via a central authority, and is optimized for open-source intelligence data through its integration with CodeX's big data solution.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
2. OBJECTIVES
• Assist medical professionals in diagnosis
• Predict probable disease and diagnosis
• Provide personalized healthcare to patients
2
3. MOTIVATION & BACKGROUND
• Too many patients but very few doctors
• Doctors short on time and overlook details
• Lab tests end up in false diagnosis
• Diagnosis is dependent on Doctor’s mood
3
4. MOTIVATION & BACKGROUND
• EMR data is not utilized properly
– Patient’s personal information and medical history
not taken in account
– Patients are often prescribed unnecessary tests
• Demographic characteristics ignored
– Existing expert systems do not take them into account
– These account for significant differences in baselines
4
5. METHODOLOGY
• Extract rules from data provided by UMDC
– This process will make use of Data mining methods
such as Neural Fuzzy learners
• Extract rules from medical literature
– Online repositories such as PubMed, Medscape, and
Wikipedia
– Crawl data from them using web crawlers such as
PHPcrawl
• Take baseline differences in account during rule
generation.
5
6. METHODOLOGY
● Generated rules will be accessible to doctors
–Through an excel spreadsheet containing results
values of lab tests
–Rules presented in a table with each row
denoting test result parameter values for each
disease
–Doctors could add and edit parameter values and
diseases without need for any programming skills
● The rules will then be converted into XML for
updating the expert system
6
7. METHODOLOGY
• Ranked list of possible diseases based on rules
and scoring
• Storage and retrieval of previous diagnosis of
patients to improve accuracy of prediction
7
9. EXTENSIONS
• Use of Symptoms during the prediction
• Medical Analysis based on demographic characteristics such
as gender, residential address etc.
• Integration of expert system with an existing Hospital EMR
• Risk monitoring system to identify patients at risk
9
10. DATA UNDERSTANDING
▪ The Blood Test Data provided by UMDC contains about 200,000 records
▪ Multiple test of about 54,000 patients
▪ Out of these, diagnosis of only 3000 is recorded
▪ Patient Tests:
10
Test
Code
Test name Normal values range
1 Haemoglobin 11.5 – 18 (mg/dl)
17 Urea 10 – 50 (mg%)
18 Creatinine 0.5 – 1.5 (mg%)
25 Potassium 3.8 – 5.2 (ME q/L)
47 Glucose Fasting 70 – 110 (mg%)
48 Glucose Random 80 – 180 (mg%)
15. DATA UNDERSTANDING
• Problems with the data
― Multiple diagnosis of patients at the same date and time
― Test codes inconsistent with the test names
e.g. Haemoglobin records are classified under test code 1 and most of the
Glucose (fasting) records are classified under test code 47. However, a few of
the Glucose (fasting) records are misclassified under test code 1
― Some of the test names are not consistent
e.g Haemoglobin test name is recorded as “Haemoglobin”, “Hb”, and
“Haemoglobin %”
― Human Errors in data entry. E.g. Temperature recorded as 980 *F (prob he
was trying to record 98.0)
15
17. DATA UNDERSTANDING
•Problems with the data
– Multiple test results values are recorded against the same registration number and the same
date and time.
17
18. DATA UNDERSTANDING
–Test Value Inconsistency- above 800 cells found with text such as ‘127 (AFTER GLOCOUSE 01
HR)’ and ‘AFTER 75GRM GLOCOUSE 01HR (92)’
18
19. DATA UNDERSTANDING
–Test Code and Test Name inconsistency problem solved by Excel formulas such
as:=IF(OR(P2="Haemoglobin %",P2="Hb"),"Haemoglobin",P2)
–And
=IF(N2="true",(MID(L2,SEARCH("(",L2)+1,SEARCH(")",L2,SEARCH("(",L2)+1)-SEARCH("(",L2)-1)),N2)
19
24. DATA CLEANING
•Handling missing values: Since a patient whose test reports are cleared will have normal test range
values. So we handled those missing values by inserting the average of normal test range values
24
27. CONCLUSION
• Aim to build a Medical Expert System to assist medical
professionals especially doctors in diagnosis
• Want to make medical literature as a direct support for
diagnosis
• Want to allow patients to be provided personalised treatment
using their medical history
• Wish to serve the medical community as Computer Scientists,
considering the field’s interdisciplinary nature
27