A review of approaches to identifying patient phenotype cohorts using electronic health records
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1 A review of approaches to identifying patient phenotype cohorts using electronic health records Shivade, Raghavan, Fosler-Lussier, Embi, Elhadad, Johnson, Lai Chaitanya Shivade JAMIA Journal Club March 6, 2014
2 Introduc>on EHR based Phenotyping Not a well defined term in literature Our scope Iden>fica>on of pa>ent cohorts using EHR Why? Clinical trial recruitment, survival analysis, etc. 2
3 Challenges Different data sources Structured Lab results, medica>ons, diagnoses, etc. Unstructured Progress notes, radiology reports, etc. Other Images, gene>c informa>on, etc. Consistency problems Data standards, mismatch, mappings. Administra>ve roadblocks Privacy 3
4 Mo>va>on Review fragmented efforts Summarize phenotypes Data sources Study Techniques Large scale systems Find opportuni>es 4
5 Literature Search Limita>ons of standard approaches Missing ar>cles Conference proceedings Manual review from Journals Journal of American Medical Informa>cs Associa>on Journal of Biomedical Informa>cs Conference Proceedings Clinical Research Informa>cs Conference AMIA Annual Symposium 5
6 Method 6
7 Criteria Inclusion Iden>fying diagnosis Clinical Trial recruitment solu>ons New methods, data- sources Exclusion No automa>on Non EHR studies Distant applica>on to cohort iden>fica>on 7
8 Top Phenotypes Drug side effect Pneumonia Asthma Hypertension Peripheral Arterial Disease Cancer Cataract Rheumatoid Arthri>s Heart Failure Diabetes 8
9 Phenotype details Cancer Breast, lung, colon, pancrea>c, brain, etc. Diabetes Type- 2, hypoglycemia, diabe>c re>nopathy. Heart failure Conges>ve heart failure, heart failure. Hypertension Hypertension, resistant hypertension. Infec>ous diseases Pneumonia, hepa>>s, pertussis, etc. 9
10 Data Sources Cancer Diabetes Heart Failure Rheumatoid Arthritis Cataract Drug Side Effect Pneumonia Asthma Peripheral Arterial Disease Hypertension Demographics Medications Lab Vitals Clinical Diagnosis Treatment Notes Genomic Other
11 Data Characteris>cs Model development Train and Test Cross valida>on Agreement sta>s>c Gold standard Manual chart review ICD- 9 codes Systema>c calcula>on using other variables Size Units: Number of pa>ents, samples, documents 11
12 Methods Rule Based Natural Language Processing Machine Learning and Sta>s>cs Hybrid Approaches 12
13 Rule Based Simple, reliable, effec>ve Not generalizable Rule deriva>on Rules based on clinical judgment Rules based on healthcare guidelines Kho et al. [2012] ADA for Type 2 Diabetes Klompas et al. [2008] CDC for Hepa>>s Refinement of previous rules Wright et al. [2011] Novel data sources Automa>cally generated rules Li et al. [2012] JBoss Drools 13
14 Natural Language Processing Notes contain valuable informa>on Abbrevia>ons, misspellings, ambiguity, etc. Approaches Named en>ty extrac>on [UMLS terms] HITex, MetaMap, GATE Use of keywords Dr. Savova, Mayo: Smoking status[2009] Drug side- effect[2011] Use of seman>c web technologies Cui et al. [2012] EpiDEA Difficult to develop. 14
15 Machine Learning & Sta>s>cs Generalize Approaches Comparison of popular algorithms idiagnosis [2011] PubMed + EHR, Compared variety. Decision tree based Explanatory models. Other algorithms Lehman et al. [2012] Topic modeling for risks in ICU pa>ents. Sta>s>cal methods Chi- squared tests, survival analysis, etc. Large datasets. 15
16 Hybrid approaches Rules and Machine learning Smoking Staus using ctakes Mul>- modal approach Pessig et al. [2012] Data mining, NLP, OCR NLP and machine learning Xu et al. [2011] Comparison. Rule Based depends on NLP NLP and rules Coden et al. [2009] IBM MedTAS 16
17 Cohort Iden>fica>on Systems Mul>- site emerge ini>a>ve Successful 14 EHR- oriented phenotyping algorithms CICTR project Lacked acceptance Single- site DISCERN at Duke STRIDE at Stanford Lessons Standard terminologies Privacy 17
18 Implica>ons for future research Terminologies Broad vs Narrow Coverage Causality Variables used for predic>on Visual models Clinical vs Informa>cs Techniques Rule Based: Automa>c rule mining NLP: End to end usage Machine learning: Explanatory models Widely accepted tools Compare in- house and open tools 18
19 Summary Fragmented efforts for same task Lack of standard solu>ons Varied data sources Comprehensive usage Varied approaches Aggrega>on is missing Some best prac>ces Standard terminologies Mul>ple data sources 19
20 This project was supported by Award Number Grant R01LM from the NaEonal Library of Medicine Thank you! state.edu Chaitanya Shivade 20
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