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

SHARPn High-Throughput Phenotyping Electronic health records (EHRs) driven phenotyping Overarching goal To develop high-throughput automated 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 ©2012 MFMER | slide-2

SHARPn High-Throughput Phenotyping Current HTP project themes Standardization of phenotype definitions Library of phenotyping algorithms Phenotyping workbench Machine learning techniques for phenotyping Just-in-time phenotyping ©2012 MFMER | slide-3

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

SHARPn High-Throughput Phenotyping 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 ( )” 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.) ©2012 MFMER | slide-5

SHARPn High-Throughput Phenotyping ©2012 MFMER | slide Meaningful Use Phase I Quality Measures

SHARPn High-Throughput Phenotyping Example: Diabetes & Lipid Mgmt. - I ©2012 MFMER | slide-7 Human readable HTML

SHARPn High-Throughput Phenotyping Example: Diabetes & Lipid Mgmt. - II ©2012 MFMER | slide-8 Computable XML

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

SHARPn High-Throughput Phenotyping Drools-based Phenotyping Architecture ©2012 MFMER | slide-10 Business Logic Clinical Element Database List of Diabetic Patients Data Access Layer Transformation Layer Inference Engine (Drools) Service for Creating Output (File, Database, etc) Transform physical representation  Normalized logical representation (Fact Model)

SHARPn High-Throughput Phenotyping Automatic translation from NQF QDM criteria to Drools ©2012 MFMER | slide-11 [Li et al., submitted 2012]

The “executable” Drools flow ©2012 MFMER | slide-12

©2012 MFMER | slide-13 Phenotype library and workbench - I 1.Converts QDM to Drools 2.Rule execution by querying the CEM database 3.Generate summary reports

©2012 MFMER | slide-14 Phenotype library and workbench - II

SHARPn High-Throughput Phenotyping ©2012 MFMER | slide-15 Phenotype library and workbench - III

SHARPn High-Throughput Phenotyping 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 ©2012 MFMER | slide-16 [Caroll et al. 2011]

SHARPn High-Throughput Phenotyping 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 all itemsets (sets of conditions) that frequently co-occur in patients. One of those conditions should be DM. Support: # of transactions the itemset I appeared in Support({TB, DLM, ND})=3 Frequent: an itemset I is frequent, if support(I)>minsup Patien t TBDL M ND…IEC 001YYYY 002YYYY 003YY 004Y 005YYY X: infrequent [Simon et al. 2012]

Electronic Health Records and Phenomics Just-in-Time phenotyping - I Transfusion-related Acute Lung Injury (TRALI) Transfusion-associated Circulatory Overload (TACO)

SHARPn High-Throughput Phenotyping Just-in-Time phenotyping - II ©2012 MFMER | slide-19 TRALI/TACO “sniffer”

Electronic Health Records and Phenomics

SHARPn High-Throughput Phenotyping 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.

SHARPn High-Throughput Phenotyping Publications till date (conservative) ©2012 MFMER | slide-22

SHARPn High-Throughput Phenotyping 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 ©2012 MFMER | slide-23

SHARPn High-Throughput Phenotyping 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) ©2012 MFMER | slide-24

SHARPn High-Throughput Phenotyping 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 Guergana Savova, Peter Szolovits Intermountain Healthcare/University of Utah Susan Welch, Herman Post, Darin Wilcox, Peter Haug Mayo Clinic Cory Endle, Rick Kiefer, Sahana Murthy, Gopu Shrestha, Dingcheng Li, Gyorgy Simon, Matt Durski, Craig Stancl, Kevin Peterson, Cui Tao, Lacey Hart, Erin Martin, Kent Bailey, Scott Tabor, Chris Chute ©2012 MFMER | slide-25