Using CPCSSN data for predictive analytics at point-of-care Karim Vassanji Karim Keshavjee.

Slides:

Advertisements

Similar presentations

Data warehousing and Data mining – an overview Dr. Suman Bhusan Bhattacharyya MBBS, ADHA, MBA.

Advertisements

Supporting End-User Access

Big Data & Predictive Analytics Michael Stencl. Agenda  Big Data  Predictive Analytics  So what?

Faculty/Presenter Disclosure Faculty: [Speaker’s name] Relationships with commercial interests: – Grants/Research Support: PharmaCorp ABC – Speakers Bureau/Honoraria:

Health Analytics in Canada – A Blueprint for Gaining Clinical Buy-in and Creating Value from the Bottom up Alex J Mair, Director, Emerging Technology Group.

Faculty of Computer Science © 2006 CMPUT 605February 11, 2008 A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll.

Prediction Models in Medicine Clinical Decision Support The Road Ahead Chapter 10.

Psychology 202b Advanced Psychological Statistics, II February 10, 2011.

Lecture 14 – Neural Networks

Shipi Kankane Prashanth Nakirekommula.  Applying analytics and risk- management capabilities to health insurance through LexisNexis data platforms. 

Chapter 9 DATA WAREHOUSING Transparencies © Pearson Education Limited 1995, 2005.

DATA WAREHOUSING.

Clementine Server Clementine Server A data mining software for business solution.

Optimal Adaptation for Statistical Classifiers Xiao Li.

Data Mining – Intro.

Neural Networks in Data Mining “An Overview”

Computer Science Universiteit Maastricht Institute for Knowledge and Agent Technology Data mining and the knowledge discovery process Summer Course 2005.

Electronic Medical Record Use and the Quality of Care in Physician Offices National Conference on Health Statistics August 17, 2010 Chun-Ju (Janey) Hsiao,

OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.

Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.

Copyright © 2013 Canada Health Infoway

DATA MINING Team #1 Kristen Durst Mark Gillespie Banan Mandura University of DaytonMBA APR 09.

Data Mining and Knowledge Discovery in Databases.

1 Business Administrators of today and tomorrow need, along with their business knowledge, analytic insight and understanding, as well the ability.

ANALYTICS BUSINESS INTELLIGENCE SOFTWARE STATISTICS Kreara Solutions | 9 years | 60 members | ISO 9001:2008.

Hospital Report Manager (HRM): Hospital Reports From Paper to Pixels A Clinician’s Experience. Carol Martyn, Office Manager Peer Lead, OntarioMD Dr.

Introduction to Data Mining Group Members: Karim C. El-Khazen Pascal Suria Lin Gui Philsou Lee Xiaoting Niu.

INTRODUCTION TO DATA MINING MIS2502 Data Analytics.

Knowledge Discovery and Data Mining Evgueni Smirnov.

1 SUPPORT VECTOR MACHINES İsmail GÜNEŞ. 2 What is SVM? A new generation learning system. A new generation learning system. Based on recent advances in.

Introduction to SQL Server Data Mining Nick Ward SQL Server & BI Product Specialist Microsoft Australia Nick Ward SQL Server & BI Product Specialist Microsoft.

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO.

Data Mining Knowledge on rough set theory SUSHIL KUMAR SAHU.

1 Machine Learning 1.Where does machine learning fit in computer science? 2.What is machine learning? 3.Where can machine learning be applied? 4.Should.

Guest Lecture Introduction to Data Mining Dr. Bhavani Thuraisingham September 17, 2010.

Copyright © 2012, SAS Institute Inc. All rights reserved. ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY,

27-18 września Data Mining dr Iwona Schab. 2 Semester timetable ORGANIZATIONAL ISSUES, INDTRODUCTION TO DATA MINING 1 Sources of data in business,

1 STAT 5814 Statistical Data Mining. 2 Use of SAS Data Mining.

BY SANDY. WHAT IS DATAMINING TYPES OF DATAMINING TOOLS OVERVIEW OF TIBCO TIBCO SPOTFIRE MINER DATA ANALYSIS EXPLORE DATA MANIPULATE DATA CHART VIEW.

1 Introduction to Data Mining C hapter 1. 2 Chapter 1 Outline Chapter 1 Outline – Background –Information is Power –Knowledge is Power –Data Mining.

MIS2502: Data Analytics Advanced Analytics - Introduction.

Big Data – Big Opportunity Mohammad Khansari ITRC President Jan 2015 ITRC, Tehran, Iran.

The Opioid Addiction Treatment Database: Using administrative health data to conduct socially accountable research in the North Joseph K. Eibl, PhD and.

Chong Ho Yu.  Data mining (DM) is a cluster of techniques, including decision trees, artificial neural networks, and clustering, which has been employed.

WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.

Developing outcome prediction models for acute intracerebral hemorrhage patients: evaluation of a Support Vector Machine based method A. Jakab 1, L. Lánczi.

Artificial Neural Networks for Data Mining. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 6-2 Learning Objectives Understand the.

1 Seattle University Master’s of Science in Business Analytics Key skills, learning outcomes, and a sample of jobs to apply for, or aim to qualify for,

Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 28 Data Mining Concepts.

Introduction to Azure Machine Learning and Data Mining algorithms Oleksandr Krakovetskyi CEO, DevRain Solutions PhD, Microsoft Regional

TOP DATA MINING TOOLS Advanced Miner.

Machine Learning & Deep Learning

RADIAL BASIS FUNCTION NEURAL NETWORK DESIGN

Annual Income Prediction Modeling Using SVM

Machine Learning & Data Science

Feature Engineering Studio Special Session

Luke Do, Jessica Olmedo, Arely Romero, and Vianca Santana

Primary care physicians visited chiefly by black patients were more likely to report they were unable to provide high-quality care to all their patients.

Faculty/Presenter Disclosure

Creating A Snakebite Order Set (Blank Template Example) Customize Your Order Set to the Type of Snakebites You See and to Your Patient Population.

Supporting End-User Access

Analytics: Its More than Just Modeling

Faculty of Science IT Department Lecturer: Raz Dara MA.

Only 28% of U.S. Primary Care Physicians Have Electronic Medical Records; Only 19% Advanced IT Capacity, 2006 Percent reporting 7 or more out of 14 functions*

Scalable and accurate deep learning with electronic health

Welcome! Knowledge Discovery and Data Mining

Machine learning analysis for predicting survival in stage III non-small cell lung cancer patients receiving definitive chemotherapy and proton radiation.

Faculty/Presenter Disclosure

Is Statistics=Data Science

Presentation transcript:

Using CPCSSN data for predictive analytics at point-of-care Karim Vassanji Karim Keshavjee

Faculty/Presenter Disclosure Faculty: KARIM VASSANJI Nothing to disclose CFPC CoI Templates Karim Vassanji Karim Keshavjee

Are Primary Care Physicians Practicing High Definition Medicine Using Black And White Technology? Karim Vassanji Karim Keshavjee

The EMR landscape in primary care in Canada Karim Vassanji Karim Keshavjee

The EMR landscape in primary care  Designed for Record Keeping  Structured Data  Unstructured Data  Some Capability for Patient Population Management  Reminders  Queries  Require Clean Data Karim Vassanji Karim Keshavjee

Can We Manage Patient Populations with EMRs? Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee

Managing patient populations with EMRs  Difficult and Imprecise  False positives  False negatives  Reminders and Queries  Alert Fatigue  Slows Down EMR Dirty Data Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee

Potential Solutions? Karim Vassanji Karim Keshavjee

Pan Canadian Initiative  Extracts EMR data from 10 different EMRs  Case finding algorithms  Cleaning algorithms  Statistical De-identification  Structured database  Available for research  De-identified data  Paper reports sent to Physicians Karim Vassanji Karim Keshavjee

EMR 1 EMR 2 EMR n Karim Vassanji Karim Keshavjee

Could CPCSSN Bring Some Color To High Definition Primary Care Medicine? Karim Vassanji Karim Keshavjee

PatientEncountersProviders VitalsHealth Card MedsLabsProcReferralsAllergiesVaccines Current CPCSSN dataset structure needs to be transformed to support advanced analytics Karim Vassanji Karim Keshavjee

The transformation… Karim Vassanji Karim Keshavjee

Data Transformation CPCSSN Analytics Database Karim Vassanji Karim Keshavjee

Predictive Analytics 101 Karim Vassanji Karim Keshavjee

 Regression Models  Linear Regression  Logistic Regression  Multinomial Logistic Regression  Time Series Models  Survival Analysis  Multivariate Adaptive Regression Splines  Machine Learning  Neural Networks  Radial Basis Functions  Support Vector Machines  Angoss Knowledge Studio  Predixion  KXEN Modeler  TIBCO Spotfire  SAS  SPSS CPCSSN Analytics Database Karim Vassanji Karim Keshavjee

The sequence… Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee

CPCSSN Analytics Database Karim Vassanji Karim Keshavjee

Karim Vassanji Karim Keshavjee