Project Lead: Jyotishman Pathak, PhD PI: Christopher G. Chute, MD, DrPH

1. Strategic Health IT Advanced Research Projects (SHARP) Area 4: Secondary Use of EHR Data Project 3: High-Throughput Phenotyping Project Lead: Jyotishman Pathak, PhD PI: Christopher G. Chute, MD, DrPH June 12, 2012

2. Electronic health records (EHRs) driven phenotyping • Overarching goal • To develop high-throughputautomated techniques and algorithms that operate on normalized EHR data to identify cohorts of potentially eligible subjects on the basis of disease, symptoms, or related findings

3. Current HTP project themes • Standardization of phenotype definitions • Library of phenotyping algorithms • Phenotyping workbench • Machine learning techniques for phenotyping • Just-in-time phenotyping

4. Algorithm Development Process - Modified • Standardized and structured representation of phenotype definition criteria • Use the NQF Quality Data Model (QDM) Rules • Conversion of structured phenotype criteria into executable queries • Use JBoss® Drools (DRLs) Semi-Automatic Execution Evaluation Phenotype Algorithm Visualization • Standardized representation of clinical data • Create new and re-use existing clinical element models (CEMs) Data Transform Transform [Welch et al. 2012] [Thompson et al., submitted 2012] [Li et al., submitted 2012] Mappings NLP, SQL

5. NQF Quality Data Model (QDM) • Standard of the National Quality Forum (NQF) • A structure and grammar to represent quality measures in a standardized format • Groups of codes in a code set (ICD-9, etc.) • "Diagnosis, Active: steroid induced diabetes" using "steroid induced diabetes Value Set GROUPING (2.16.840.1.113883.3.464.0001.113)” • Supports temporality & sequences • AND: "Procedure, Performed: eye exam" > 1 year(s) starts before or during "Measurement end date" • Implemented as set of XML schemas • Links to standardized terminologies (ICD-9, ICD-10, SNOMED-CT, CPT-4, LOINC, RxNorm etc.)

6. 116 Meaningful Use Phase I Quality Measures

7. Example: Diabetes & Lipid Mgmt. - I Human readable HTML

8. Example: Diabetes & Lipid Mgmt. - II Computable XML

9. Algorithm Development Process - Modified • Standardized and structured representation of phenotype definition criteria • Use the NQF Quality Data Model (QDM) Rules • Conversion of structured phenotype criteria into executable queries • Use JBoss® Drools (DRLs) Semi-Automatic Execution Evaluation Phenotype Algorithm Visualization • Standardized representation of clinical data • Create new and re-use existing clinical element models (CEMs) Data Transform Transform [Welch et al. 2012] [Thompson et al., submitted 2012] [Li et al., submitted 2012] Mappings NLP, SQL

10. Drools-based Phenotyping Architecture Clinical Element Database Data Access Layer Business Logic Transformation Layer Inference Engine (Drools) List of Diabetic Patients Service for Creating Output (File, Database, etc) Transform physical representation  Normalized logical representation (Fact Model)

11. Automatic translation from NQF QDM criteria to Drools [Li et al., submitted 2012]

12. The “executable” Drools flow

13. Phenotype library and workbench - I http://phenotypeportal.org Converts QDM to Drools Rule execution by querying the CEM database Generate summary reports

14. Phenotype library and workbench - II http://phenotypeportal.org

15. Phenotype library and workbench - III

16. Machine learning and HTP - I • Machine learning and association rule mining • Manual creation of algorithms take time • Let computers do the “hard work” • Validate against expert developed ones [Caroll et al. 2011]

17. Machine learning and HTP - II • Origins from sales data • Items (columns): co-morbid conditions • Transactions (rows): patients • Itemsets: sets of co-morbid conditions • Goal: find allitemsets (sets of conditions) that frequently co-occur in patients. • One of those conditions should be DM. • Support: # of transactions the itemsetI appeared in • Support({TB, DLM, ND})=3 • Frequent: an itemsetI is frequent, if support(I)>minsup X: infrequent [Simon et al. 2012]

18. Just-in-Time phenotyping - I Transfusion-related Acute Lung Injury (TRALI) Transfusion-associated Circulatory Overload (TACO)

19. Just-in-Time phenotyping - II TRALI/TACO “sniffer”

21. Active Surveillance for TRALI and TACO Of the 88 TRALI cases correctly identified by the CART algorithm, only 11 (12.5%) of these were reported to the blood bank by the clinical service. Of the 45 TACO cases correctly identified by the CART algorithm, only 5 (11.1%) were reported to the blood bank by the clinical service.

22. Publications till date (conservative)

23. 2011 Milestones • Standardized definitions for phenotype criteria • Rules-based environment for phenotype algorithm execution • National library for standardized phenotype definitions (collaboration with eMERGE) • Machine learning techniques for algorithm definitions • Online, real-time phenotype execution • Phenotyping algorithm authoring environment

24. 2012 Milestones • Machine learning techniques for algorithm definitions • Online, real-time phenotype execution • Collaboration with NQF, Query Health and i2b2 infrastructures • Use cases and demonstrations • MU quality metrics (w/ NQF, Query Health) • Cohort identification (w/ eMERGE, PGRN) • Value analysis (w/ Mayo CSHCD, REP) • Clinical trial alerting (w/ Mayo Cancer Ctr./CTSA)

25. Project 3: Collaborators & Acknowledgments • CDISC (Clinical Data Interchange Standards Consortium) • Rebecca Kush, Landen Bain • Centerphase Solutions • Gary Lubin, Jeff Tarlowe • Group Health Seattle • David Carrell • Harvard University/MIT • GuerganaSavova, Peter Szolovits • Intermountain Healthcare/University of Utah • Susan Welch, Herman Post, Darin Wilcox, Peter Haug • Mayo Clinic • Cory Endle, Rick Kiefer, Sahana Murthy, GopuShrestha, Dingcheng Li, Gyorgy Simon, Matt Durski, Craig Stancl, Kevin Peterson, Cui Tao, Lacey Hart, Erin Martin, Kent Bailey, Scott Tabor, Chris Chute