Workflow framework for mining Diagnostic rules from ehrs Roxana Danger
|
|
|
- Osborn Sherman
- 8 years ago
- Views:
Transcription
1 Workflow framework for mining Diagnostic rules from ehrs Roxana Danger Department of Computing Imperial College London KNIME User Day, University of Westminster, June 25th, 2013
2 Outline EU-FP7 TRANSFoRm project Universal repository of diagnostic rules Data type heterogeneity Definition of analysis goals Selection of algorithms and quality measures Selection of working environment Results presentation Summary 2
3 TRANSFoRm Translational Research & Patient Safety in Europe 3
4 Knowledge In Healthcare and TRANSFoRm role Specific Research Knowledge Produced from clinical trials From controlled populations With well-defined questions Routinely Collected Knowledge Actionable Knowledge A vast quantity of data Captured in ehr systems With large population coverage May lack in detail and quality Distilled scientific findings Usable in clinical practice To support decision making 4
5 TRANSFoRm aims and objectives TRANSFoRm will develop a digital infrastructure that facilitates the reuse of primary care real world electronic Health Records (ehr) data to improve both patient safety and the conduct and volume of Clinical Research in Europe. The project will drive the advanced integration of clinical practice and research data to: Support clinical research with participant identification and evaluation of outcomes Support epidemiological research with large scale phenotype-genotype association studies and follow-up on trials Support clinical care on diagnosis and monitoring of patients 5
6 TRANSFoRm Data mining ehr BDs Data Mining Preproc Dataset Mining MCERs Filtering Filtered MCERs DSS Repository Updating Manual reviewed, interpretation and creation of CPRs Initial set of CPRs Validation CPRs DSS Repository CPRs from bibliog. 6
7 MCERs Measured clinical evidence rules Derek, 2012 DemographicFeatures, RFEs, Symptoms, Signs, Riskfactors, Test performed Diagnosis(QM) 7
8 Data from GPRD, TRANSHIS, NIVEL 8
9 ehrs Data mining Data type heterogeneity CDIM Terminologies and their mappings EHR BD 1 DSM 1 C D I M CDIM - DSM 1 C D I M CDIM - Dataset 1 EHR BD 2 EHR BD 3 DSM 2 DSM 3 M a p p i n g CDIM - DSM 2 CDIM - DSM 3 Q u e r y CDIM - Dataset 2 CDIM - Dataset 3 9
10 Definition of analysis goals First consultation of an EoC DemographicFeatures, RFEs, Symptoms, Signs, Riskfactors Diagnosis(QM) Consequent consultations of an EoC DemographicFeatures, RFEs, Symptoms, Signs, Riskfactors, Test performed, Time from previous consultation Diagnosis(QM) (DemographicFeatures, RFEs, Symptoms, Signs, Riskfactors, Test performed, Time from previous consultation) (DemographicFeatures, RFEs, Symptoms, Signs, Riskfactors, Test performed, Time from previous consultation) Diagnosis(QM) 10
11 Algorithms and quality measures (1) Fast implementations, easily parallelizable Easy to understand outputs, from which MCERs can be easily extracted. Association rules (Apriori, Kingfisher) Decision trees (C4.5) Sequential patterns (Sequential Kingfisher) 11
12 Algorithms and quality measures (2) Part I: Consequent (disease) interest Prior probability Part III: Itemsets characterization Support Error rate Part II: Variables characterization Posterior probability (+/-) Likelihood ratio Odd Ratio Part IV: Rules characterization Lift Confidence Sensitivity Specificity Likelihood ratio Odd Ratio 12
13 KNIME workflow environment (1) Our KNIME extensions: Database nodes Filter rows Filter columns Rename Resort columns Derive Group by Join Data mining nodes Kingfisher Quantifiers 13
14 KNIME workflow environment (1) 14
15 KNIME workflow environment (2) Configuring 15
16 KNIME workflow environment (4) Model learners 16
17 KNIME workflow environment (4) Quality measures computing 17
18 Results presentation /RulesAssessment/#rulesViewer 18
19 KNIME workflow for text mining 19
20 Summary Universal repository of diagnostic rules Data type heterogeneity Definition of analysis goals Selection of algorithms and quality measures Selection of working environment Results presentation Workflows defined (first results are promising) Future work Provenance CDIM integration 20
21 Workflow framework for mining Diagnostic rules from ehrs Thanks to team of data mining task in TRANSFoRm: Derek Connigan (RSCI) Jean K. Soler (Synapse) Tomasz Kajdanowicz (WROC) Przemyslaw Kazienko (WROC) Vasa Curcin (IC) KNIME User Day, University of Westminster, June 25th, 2013
Derek Corrigan Work Package Lead: WP4 Decision Rules and Evidence Royal College of Surgeons in Ireland
TRANSFoRm Derek Corrigan Work Package Lead: WP4 Decision Rules and Evidence Royal College of Surgeons in Ireland The Learning Health System in Europe Brussels - 25 th September 2015 This project has received
TRANSFoRm: Vision of a learning healthcare system
TRANSFoRm: Vision of a learning healthcare system Vasa Curcin, Imperial College London Theo Arvanitis, University of Birmingham Derek Corrigan, Royal College of Surgeons Ireland TRANSFoRm is partially
CREATING AND APPLYING KNOWLEDGE IN ELECTRONIC HEALTH RECORD SYSTEMS. Prof Brendan Delaney, King s College London
CREATING AND APPLYING KNOWLEDGE IN ELECTRONIC HEALTH RECORD SYSTEMS Prof Brendan Delaney, King s College London www.transformproject.eu 7.5M European Commission March 2010-May 2015 Funded under the Patient
Didacticiel Études de cas. Association Rules mining with Tanagra, R (arules package), Orange, RapidMiner, Knime and Weka.
1 Subject Association Rules mining with Tanagra, R (arules package), Orange, RapidMiner, Knime and Weka. This document extends a previous tutorial dedicated to the comparison of various implementations
Translational Medicine and Patient Safety in Europe
TRANSFoRm Translational Medicine and Patient Safety in Europe Prof Brendan Delaney King s College London, Co-ordinator and Scientific Director, on behalf of the project steering ctte This project is partially
Theodoros. N. Arvanitis, RT, DPhil, CEng, MIET, MIEEE, AMIA, FRSM
TRANSFoRm Theodoros. N. Arvanitis, RT, DPhil, CEng, MIET, MIEEE, AMIA, FRSM Biomedical Informatics, Signals & Systems Research Laboratory School of Electronic, Electrical & Computer Engineering College
TRANSFoRm: TRANSFoRming health care research and its implementation
TRANSFoRm: TRANSFoRming health care research and its implementation Frank Sullivan, on behalf of the TRANSFoRm Consortium. Health Informatics Centre Dundee TRANSFoRm is partially funded by the European
KNIME TUTORIAL. Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it
KNIME TUTORIAL Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it Outline Introduction on KNIME KNIME components Exercise: Market Basket Analysis Exercise: Customer Segmentation Exercise:
The Learning Healthcare System: a European perspective
The Learning Healthcare System: a European perspective Brendan Delaney Wolfson Professor of General Practice, King s College London Challenges of the EBP Paradigm Clinical Research in crisis Hard to identify
D3.5 Software Integration Plan
Translational Research and Patient Safety in Europe The TRANSFoRm Project is partially funded by the European Commission under the 7 th Framework Programme Grant Agreement Number FP7-247787 D3.5 Software
CHAPTER 3 PROPOSED SCHEME
79 CHAPTER 3 PROPOSED SCHEME In an interactive environment, there is a need to look at the information sharing amongst various information systems (For E.g. Banking, Military Services and Health care).
In this presentation, you will be introduced to data mining and the relationship with meaningful use.
In this presentation, you will be introduced to data mining and the relationship with meaningful use. Data mining refers to the art and science of intelligent data analysis. It is the application of machine
Health Foundations Module
FSA GROUP AND HEALTH TRACK Health Foundations Module SECTION 1: MODULE OVERVIEW The Financial and Health Economics Module discusses a macro view of the health care system. This module, Health Foundations,
Bench to Bedside Clinical Decision Support:
Bench to Bedside Clinical Decision Support: The Role of Semantic Web Technologies in Clinical and Translational Medicine Tonya Hongsermeier, MD, MBA Corporate Manager, Clinical Knowledge Management and
Clinic + - A Clinical Decision Support System Using Association Rule Mining
Clinic + - A Clinical Decision Support System Using Association Rule Mining Sangeetha Santhosh, Mercelin Francis M.Tech Student, Dept. of CSE., Marian Engineering College, Kerala University, Trivandrum,
2 Decision tree + Cross-validation with R (package rpart)
1 Subject Using cross-validation for the performance evaluation of decision trees with R, KNIME and RAPIDMINER. This paper takes one of our old study on the implementation of cross-validation for assessing
Association Technique on Prediction of Chronic Diseases Using Apriori Algorithm
Association Technique on Prediction of Chronic Diseases Using Apriori Algorithm R.Karthiyayini 1, J.Jayaprakash 2 Assistant Professor, Department of Computer Applications, Anna University (BIT Campus),
Research Article Translational Medicine and Patient Safety in Europe: TRANSFoRm Architecture for the Learning Health System in Europe
Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 961526, 8 pages http://dx.doi.org/10.1155/2015/961526 Research Article Translational Medicine and Patient Safety in
What s Cooking in KNIME
What s Cooking in KNIME Thomas Gabriel Copyright 2015 KNIME.com AG Agenda Querying NoSQL Databases Database Improvements & Big Data Copyright 2015 KNIME.com AG 2 Querying NoSQL Databases MongoDB & CouchDB
Prediction of Heart Disease Using Naïve Bayes Algorithm
Prediction of Heart Disease Using Naïve Bayes Algorithm R.Karthiyayini 1, S.Chithaara 2 Assistant Professor, Department of computer Applications, Anna University, BIT campus, Tiruchirapalli, Tamilnadu,
Better Healthcare with Data Mining
Technical report Better Healthcare with Data Mining Philip Baylis Shared Medical Systems Limited, UK Table of contents Abstract... 2 Introduction... 2 Inpatient length of stay... 2 Patient data... 3 Detect
Electronic Medical Records Getting It Right and Going to Scale
Electronic Medical Records Getting It Right and Going to Scale W. Ed Hammond, Ph.D. Duke University Medical Center 02/03/2000 e-hammond, Duke 0 Driving Factors Patient Safety Quality Reduction in cost
Knowledge Discovery and Data Mining
Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right
LEVERAGING CLINICAL ANALYTICS TO DESIGN PERSONALISED MEDICINE. Dr. S. B. Bhattacharyya MBBS, MBA Healthcare Domain Consultant
LEVERAGING CLINICAL ANALYTICS TO DESIGN PERSONALISED MEDICINE Dr. S. B. Bhattacharyya MBBS, MBA Healthcare Domain Consultant LEVERAGING CLINICAL ANALYTICS TO DESIGN PERSONALISED MEDICINE How clinical analytics
Distributed Networking
Distributed Networking Millions of people. Strong collaborations. Privacy first. Jeffrey Brown, Lesley Curtis, Richard Platt Harvard Pilgrim Health Care Institute and Harvard Medical School Duke Medical
KnowledgeSEEKER Marketing Edition
KnowledgeSEEKER Marketing Edition Predictive Analytics for Marketing The Easiest to Use Marketing Analytics Tool KnowledgeSEEKER Marketing Edition is a predictive analytics tool designed for marketers
Hitachi s Plans for Healthcare IT Services
Hitachi Review Vol. 63 (2014), No. 1 41 Hitachi s Plans for Healthcare IT Services Masaru Morishita Kenichi Araki Koichiro Kimotsuki Satoshi Mitsuyama OVERVIEW: The soaring cost of healthcare has become
Copyright 2005-2010 Soleran, Inc. esalestrack On-Demand CRM. Trademarks and all rights reserved. esalestrack is a Soleran product Privacy Statement
DATA MINING AND REPORTING IN HEALTHCARE
DATA MINING AND REPORTING IN HEALTHCARE Divya Gandhi 1, Pooja Asher 2, Harshada Chaudhari 3 1,2,3 Department of Information Technology, Sardar Patel Institute of Technology, Mumbai,(India) ABSTRACT The
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Spring 2015 Thomas Hill, Ph.D. VP Analytic Solutions Dell Statistica Overview and Agenda Dell Software overview Dell in
Mining an Online Auctions Data Warehouse
Proceedings of MASPLAS'02 The Mid-Atlantic Student Workshop on Programming Languages and Systems Pace University, April 19, 2002 Mining an Online Auctions Data Warehouse David Ulmer Under the guidance
An intelligent tool for expediting and automating data mining steps. Ourania Hatzi, Nikolaos Zorbas, Mara Nikolaidou and Dimosthenis Anagnostopoulos
An intelligent tool for expediting and automating data mining steps Ourania Hatzi, Nikolaos Zorbas, Mara Nikolaidou and Dimosthenis Anagnostopoulos Outline Data Mining, current tools An intelligent tool
Connecting Basic Research and Healthcare Big Data
Elsevier Health Analytics WHS 2015 Big Data in Health Connecting Basic Research and Healthcare Big Data Olaf Lodbrok Managing Director Elsevier Health Analytics o.lodbrok@elsevier.com t +49 89 5383 600
ICT Perspectives on Big Data: Well Sorted Materials
ICT Perspectives on Big Data: Well Sorted Materials 3 March 2015 Contents Introduction 1 Dendrogram 2 Tree Map 3 Heat Map 4 Raw Group Data 5 For an online, interactive version of the visualisations in
Big Data Analytics and Healthcare
Big Data Analytics and Healthcare Anup Kumar, Professor and Director of MINDS Lab Computer Engineering and Computer Science Department University of Louisville Road Map Introduction Data Sources Structured
Better Business Through Data Analysis & Monitoring
CaseWare Analytics is an industry leader in providing technology solutions for audit and finance professionals, with over 400,000 users worldwide. Better Business Through Data Analysis & Monitoring 469
KNIME opens the Doors to Big Data. A Practical example of Integrating any Big Data Platform into KNIME
KNIME opens the Doors to Big Data A Practical example of Integrating any Big Data Platform into KNIME Tobias Koetter Rosaria Silipo Tobias.Koetter@knime.com Rosaria.Silipo@knime.com 1 Table of Contents
Mercy Health System. St. Louis, MO. Process Mining of Clinical Workflows for Quality and Process Improvement
Mercy Health System St. Louis, MO Process Mining of Clinical Workflows for Quality and Process Improvement Paul Helmering, Executive Director, Enterprise Architecture Pete Harrison, Data Analyst, Mercy
Security Middleware Infrastructure for Medical Imaging System Integration
Security Middleware Infrastructure for Medical Imaging System Integration Weina Ma, Kamran Sartipi, Hassan Sharghi Department of Electrical, Computer and Software Engineering, University of Ontario Institute
Applying Data Mining of Fuzzy Association Rules to Network Intrusion Detection
Applying Data Mining of Fuzzy Association Rules to Network Intrusion Detection Authors: Aly El-Semary, Janica Edmonds, Jesús González-Pino, and Mauricio Papa Center for Information Security Department
HETEROGENEOUS DATA INTEGRATION FOR CLINICAL DECISION SUPPORT SYSTEM. Aniket Bochare - aniketb1@umbc.edu. CMSC 601 - Presentation
HETEROGENEOUS DATA INTEGRATION FOR CLINICAL DECISION SUPPORT SYSTEM Aniket Bochare - aniketb1@umbc.edu CMSC 601 - Presentation Date-04/25/2011 AGENDA Introduction and Background Framework Heterogeneous
1. Classification problems
Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification
Introduction to openehr Archetypes & Templates. Dr Ian McNicoll Dr Heather Leslie
Introduction to openehr Archetypes & Templates Dr Ian McNicoll Dr Heather Leslie Traditional Application Development Clinical Knowledge Data Model Ocean Informatics 2010 Tradi&onal Informa&on model 2 level
PHARMACEUTICAL BIGDATA ANALYTICS
PHARMACEUTICAL BIGDATA ANALYTICS ANDINSIGHTS December 2013 Strategic Research Insights, Inc. 2013 Sources of Big Data in rpharmaceutical and Healthcare Industry Challenges with Big Data in Pharma Oncology
How can you unlock the value in real-world data? A novel approach to predictive analytics could make the difference.
How can you unlock the value in real-world data? A novel approach to predictive analytics could make the difference. What if you could diagnose patients sooner, start treatment earlier, and prevent symptoms
Data modelling methods in clinical trials: Experiences from the CTMND project (ctmnd.org)
Data modelling methods in clinical trials: Experiences from the CTMND project (ctmnd.org) Athanasios Anastasiou, Emmanuel Ifeachor, John Zajicek & the CTMND Consortium University of Plymouth Peninsula
Big Data. Fast Forward. Putting data to productive use
Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize
D A T A M I N I N G C L A S S I F I C A T I O N
D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.
SAP InfiniteInsight 7.0 SP1
End User Documentation Document Version: 1.0-2014-11 Getting Started with Social Table of Contents 1 About this Document... 3 1.1 Who Should Read this Document... 3 1.2 Prerequisites for the Use of this
Data Mining Fundamentals
Part I Data Mining Fundamentals Data Mining: A First View Chapter 1 1.11 Data Mining: A Definition Data Mining The process of employing one or more computer learning techniques to automatically analyze
Il lavoro di armonizzazione. e HL7
Il lavoro di armonizzazione tra CEN 13606, openehr e HL7 Dr Dipak Kalra Centre for Health Informatics and Multiprofessional Education (CHIME) University College London d.kalra@chime.ucl.ac.uk Drivers for
Clinical Decision Support Systems An Open Source Perspective
Decision Support Systems An Open Source Perspective John McKim CTO, Knowledge Analytics Incorporated john@knowledgeanalytics.com http://www.knowledgeanaytics.com OSEHRA Open Source Summit 2014 Agenda CDS
Ensembles and PMML in KNIME
Ensembles and PMML in KNIME Alexander Fillbrunn 1, Iris Adä 1, Thomas R. Gabriel 2 and Michael R. Berthold 1,2 1 Department of Computer and Information Science Universität Konstanz Konstanz, Germany First.Last@Uni-Konstanz.De
Semantically Steered Clinical Decision Support Systems
Semantically Steered Clinical Decision Support Systems By Eider Sanchez Herrero Department of Computer Science and Artificial Intelligence University of the Basque Country Advisors Prof. Manuel Graña Romay
More details on the inputs, functionality, and output can be found below.
Overview: The SMEEACT (Software for More Efficient, Ethical, and Affordable Clinical Trials) web interface (http://research.mdacc.tmc.edu/smeeactweb) implements a single analysis of a two-armed trial comparing
Architectural Patterns: From Mud to Structure
DCC / ICEx / UFMG Architectural Patterns: From Mud to Structure Eduardo Figueiredo http://www.dcc.ufmg.br/~figueiredo From Mud to Structure Layered Architecture It helps to structure applications that
Your mission is our mission
Improving Health Is the Heart of Your Mission Today s health industry faces tough challenges. Costs continue to rise. Consumers want more control over their own health decisions. A technologically adept
SALUS: Enabling the Secondary Use of EHRs for Post Market Safety Studies
SALUS: Enabling the Secondary Use of EHRs for Post Market Safety Studies May 2015 A. Anil SINACI, Deputy Project Coordinator SALUS: Scalable, Standard based Interoperability Framework for Sustainable Proactive
KNIME Enterprise server usage and global deployment at NIBR
KNIME Enterprise server usage and global deployment at NIBR Gregory Landrum, Ph.D. NIBR Informatics Novartis Institutes for BioMedical Research, Basel 8 th KNIME Users Group Meeting Berlin, 26 February
Enabling the Big Data Commons through indexing of data and their interactions
biomedical and healthcare Data Discovery Index Ecosystem Enabling the Big Data Commons through indexing of and their interactions 2 nd BD2K all-hands meeting Bethesda 11/12/15 Aims 1. Help users find accessible
Research Agenda for General Practice / Family Medicine and Primary Health Care in Europe Summary EGPRN
Research Agenda for General Practice / Family Medicine and Primary Health Care in Europe Summary EGPRN EUROPEAN GENERAL PRACTICE RESEARCH NETWO RK EGPRN is a network organisation within WONCA Region Europe
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 21/2012, ISSN 1642-6037
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 21/2012, ISSN 1642-6037 FDA, medical software, recall, safety of medical devices. Leszek DREWNIOK 1, Ewelina PIEKAR 1, Mirosław STASIAK 1, Remigiusz MANIURA
Keywords data mining, prediction techniques, decision making.
Volume 5, Issue 4, April 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analysis of Datamining
IBM's Fraud and Abuse, Analytics and Management Solution
Government Efficiency through Innovative Reform IBM's Fraud and Abuse, Analytics and Management Solution Service Definition Copyright IBM Corporation 2014 Table of Contents Overview... 1 Major differentiators...
Welcome! E-Health and Data Analytics: Behavioral Health
Welcome! E-Health and Data Analytics: Behavioral Health For the duration of this presentation, please have your cell phones available. You will be asked to use them. 2014 Poll Everywhere Polling application
How To Write An Electronic Health Record
EHR Requirements David LLOYD and Dipak KALRA CHIME Centre for Health Informatics and Multiprofessional Education, University College London N19 5LW, by email: d.lloyd@chime.ucl.ac.uk. Abstract. Published
A Review of Data Mining Techniques
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,
Two-Phase Data Warehouse Optimized for Data Mining
Two-Phase Data Warehouse Optimized for Data Mining Balázs Rácz András Lukács Csaba István Sidló András A. Benczúr Data Mining and Web Search Research Group Computer and Automation Research Institute Hungarian
Decision Support for Quality Improvement. Objective. Clinical Decision Support (CDS) Meaningful Use
Decision Support for Quality Improvement Unit 6b: Clinical Decision Support Systems that Help Improve Quality This material was developed by Johns Hopkins University, funded by the Department of Health
Archive I. Metadata. 26. May 2015
Archive I Metadata 26. May 2015 2 Norstore Data Management Plan To successfully execute your research project you want to ensure the following three criteria are met over its entire lifecycle: You are
Person Responsible for Module (Name, Mail address): Dr. Javier Soriano, jsoriano@fi.upm.es
Name of Module: Data Science ECTS: Module-ID: Seminars 4.5 xxx Person Responsible for Module (Name, Mail address): Dr. Javier Soriano, jsoriano@fi.upm.es University: UPM Departments: DLSIIS, DIA, DATSI
Find the signal in the noise
Find the signal in the noise Electronic Health Records: The challenge The adoption of Electronic Health Records (EHRs) in the USA is rapidly increasing, due to the Health Information Technology and Clinical
New Matrix Approach to Improve Apriori Algorithm
New Matrix Approach to Improve Apriori Algorithm A. Rehab H. Alwa, B. Anasuya V Patil Associate Prof., IT Faculty, Majan College-University College Muscat, Oman, rehab.alwan@majancolleg.edu.om Associate
warehouse landscape for HINC
Transforming the data warehouse landscape for the financial industry HINC by Graz A data warehouse pre-configured for the financial industry significantly reduces the costs and risks associated with reporting
Adventures in EHR Computable Phenotypes: Lessons Learned from the Southeastern Diabetes Initiative (SEDI)
Adventures in EHR Computable Phenotypes: Lessons Learned from the Southeastern Diabetes Initiative (SEDI) PCORnet Best Practices Sharing Session Wednesday, August 5, 2015 Introductions to the Round Table
Achilles a platform for exploring and visualizing clinical data summary statistics
Biomedical Informatics discovery and impact Achilles a platform for exploring and visualizing clinical data summary statistics Mark Velez, MA Ning "Sunny" Shang, PhD Department of Biomedical Informatics,
Conquering the Astronomical Data Flood through Machine
Conquering the Astronomical Data Flood through Machine Learning and Citizen Science Kirk Borne George Mason University School of Physics, Astronomy, & Computational Sciences http://spacs.gmu.edu/ The Problem:
Environmental Health Science. Brian S. Schwartz, MD, MS
Environmental Health Science Data Streams Health Data Brian S. Schwartz, MD, MS January 10, 2013 When is a data stream not a data stream? When it is health data. EHR data = PHI of health system Data stream
Data processing goes big
Test report: Integration Big Data Edition Data processing goes big Dr. Götz Güttich Integration is a powerful set of tools to access, transform, move and synchronize data. With more than 450 connectors,
Analytics on Big Data
Analytics on Big Data Riccardo Torlone Università Roma Tre Credits: Mohamed Eltabakh (WPI) Analytics The discovery and communication of meaningful patterns in data (Wikipedia) It relies on data analysis
Using Normalized Status Change Events Data in Business Intelligence. Mark C. Cooke, Ph.D. Tax Management Associates, Inc.
Using Normalized Status Change Events Data in Business Intelligence Mark C. Cooke, Ph.D. Tax Management Associates, Inc. A note to the audience TMA is a company that serves state and local government,
Business Intelligence. Tutorial for Rapid Miner (Advanced Decision Tree and CRISP-DM Model with an example of Market Segmentation*)
Business Intelligence Professor Chen NAME: Due Date: Tutorial for Rapid Miner (Advanced Decision Tree and CRISP-DM Model with an example of Market Segmentation*) Tutorial Summary Objective: Richard would
The Data Mining Process
Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data
Structural Health Monitoring Tools (SHMTools)
Structural Health Monitoring Tools (SHMTools) Getting Started LANL/UCSD Engineering Institute LA-CC-14-046 c Copyright 2014, Los Alamos National Security, LLC All rights reserved. May 30, 2014 Contents
Post-Implementation EMR Evaluation for the Beta Ambulatory Care Clinic Proposed Plan Jul 6/2012, Version 2.0
1. Purpose and Scope Post-Implementation EMR Evaluation for the Beta Ambulatory Care Clinic Proposed Plan Jul 6/2012, Version 2.0 This document describes our proposed plan to conduct a formative evaluation
Clinical Decision Support Systems The revolution for a better health care
Clinical Decision Support Systems The revolution for a better health care CDSS Definition Clinical Decision Support systems link health observations with health knowledge to influence health choices by
In-Database Analytics
Embedding Analytics in Decision Management Systems In-database analytics offer a powerful tool for embedding advanced analytics in a critical component of IT infrastructure. James Taylor CEO CONTENTS Introducing
Data Science. Research Theme: Process Mining
Data Science Research Theme: Process Mining Process mining is a relatively young research discipline that sits between computational intelligence and data mining on the one hand and process modeling and
Accelerating variant calling
Accelerating variant calling Mauricio Carneiro GSA Broad Institute Intel Genomic Sequencing Pipeline Workshop Mount Sinai 12/10/2013 This is the work of many Genome sequencing and analysis team Mark DePristo
ENABLING DATA TRANSFER MANAGEMENT AND SHARING IN THE ERA OF GENOMIC MEDICINE. October 2013
ENABLING DATA TRANSFER MANAGEMENT AND SHARING IN THE ERA OF GENOMIC MEDICINE October 2013 Introduction As sequencing technologies continue to evolve and genomic data makes its way into clinical use and
Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification
Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification Tina R. Patil, Mrs. S. S. Sherekar Sant Gadgebaba Amravati University, Amravati tnpatil2@gmail.com, ss_sherekar@rediffmail.com
Data Mining for Business Analytics
Data Mining for Business Analytics Lecture 2: Introduction to Predictive Modeling Stern School of Business New York University Spring 2014 MegaTelCo: Predicting Customer Churn You just landed a great analytical
Data Mining Applications in Manufacturing
Data Mining Applications in Manufacturing Dr Jenny Harding Senior Lecturer Wolfson School of Mechanical & Manufacturing Engineering, Loughborough University Identification of Knowledge - Context Intelligent
Database Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski
Mining of predictive patterns in Electronic health records data
Mining of predictive patterns in Electronic health records data Iyad Batal and Milos Hauskrecht Department of Computer Science University of Pittsburgh milos@cs.pitt.edu 1 Introduction The emergence of
A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML
www.bsc.es A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML Josep Ll. Berral, Nicolas Poggi, David Carrera Workshop on Big Data Benchmarks Toronto, Canada 2015 1 Context ALOJA: framework
Contents. Dedication List of Figures List of Tables. Acknowledgments
Contents Dedication List of Figures List of Tables Foreword Preface Acknowledgments v xiii xvii xix xxi xxv Part I Concepts and Techniques 1. INTRODUCTION 3 1 The Quest for Knowledge 3 2 Problem Description
Binary Diagnostic Tests Two Independent Samples
Chapter 537 Binary Diagnostic Tests Two Independent Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary
Improvement of the quality of medical databases: data-mining-based prediction of diagnostic codes from previous patient codes
Digital Healthcare Empowering Europeans R. Cornet et al. (Eds.) 2015 European Federation for Medical Informatics (EFMI). This article is published online with Open Access by IOS Press and distributed under
Data Domain Profiling and Data Masking for Hadoop
Data Domain Profiling and Data Masking for Hadoop 1993-2015 Informatica LLC. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or