A statistical approach to utilizing electronic health records
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1 A statistical approach to utilizing electronic health records Joseph E. Lucas, Ph. D. Copyright 2013 Quintiles
2 Know your Patients Can a health system make use of what it knows about patients to make informed decisions? Lots of data. What questions to ask? How to generate answers? Where is my patient going next? What will help me know? EHR data, labs, genetics, quantified self data, genomics? Where is my patient going next? 2
3 Complicated Messy Data Electronic Health Records data has Continuous data (labs, age, vitals) Categorical data (gender, race, family history) Written text (nurses and physicians notes, radiology reports) Images (x-ray, CT, EKG) Important information everywhere Example: A diabetic might have any or all of the following Synonym of diabetes in a note High lab values (glucose, HbA1C) Relevant medications Billing codes related to treatment of diabetes Predisposing demographics (weight, race, family history) Genetic predisposition (TCF7L2, JAZF1, HHEX, etc) We want to incorporate all of this information Don t want to be fooled by mistakes 3
4 Approach Automated Patients like me Create groups of homogeneous patients This allows: Automated generation of differential diagnosis Novel comparative effectiveness studies Listing of treatment options Identification of Adverse drug events Estimation of disease progression and prognosis Assessment clinical utility of novel lab tests Predict probable patient type from other data Group patients through time 4
5 Model Product densities from each component Each data type is treated independently Doesn t explicitly model correlation across different data types 5
6 Topic Model for Text i index for document j index for word k index for cluster n i topic for document i q k latent word frequencies m k # documents in cluster k w i indicator vector pain Multiply likelihood by 2/5. chest pain with exercise chest angina 6
7 Topic Models, NLP, Mixed data Clustering patient visits to identify features of disease processes Medications Asa/buffers Aspirin Hctz Lantus Lipitor Lisinopril Metoprolol Metformin Diagnoses Angina pectoris CAD Chest pain acute Chest pressure MI Palpitations Chest tightness Myocardial infarction Pericarditis Medical (NLP) Concepts No trauma No fever No diarrhea Aspirin Back Chest Dyspnea Exercise Topic Model P(w i,j n i =k)~mn(q k ) q k ~ Dir(β) Profile of a patient with heart disease Combined evidence from multiple sources Relationships between drugs and patient group Metformin is protective Lantus: heart failure is side effect Normal-Gamma Clustering P(r i n i =k) ~ N(µ k,σ k ) P(µ k,σ k ) ~ NormGamma 1. Henao, Murray, Ginsburg, Carin, Lucas (2013) Patient clustering with uncoded text in electronic medical records, Proc Annual Symposium AMIA 2. Murray, Dunson, Carin, Lucas (2013) Bayesian Gaussian copula factor models for mixed data, JASA, 108(502)
8 Nice features Very fast mixing MCMC without ever having to sample parameters Can compute MAP Model knows when it doesn t know Use to identify trends in other data Model fitted to clinical data Assess ability of new data to predict clusters New data type Example: Suppose a small percentage of your customers use your clinic. What do they purchase in the rest of the store? Does that inform on other potential clinic users? Can/should you market to them? Predict patient type based on new data 8
9 Case Study: Data 54,000 records from ED Contains Vocabularies Notes Orders Patient reported meds Diagnoses Categorical data Chief complaint Gender Disposition Zip code Continuous data Age Priority Vitals Weight None is codified All data subject to parsing errors 9
10 Messy data weakness/aching/hea daches weakness/discomfort weakness/dizziness weakness/dizziness/re cent weakness/faintness/c ongestion weakness/fatigue weakness/flaccid weakness/heaviness weakness/numbness weakness/pain weakness/shaking weakness/tingling weakness; weaknessambulatory weaknesscoughfeverl wbswent weaknesscoughing weaknessdiahrrea weaknessdizziness weaknesses weaknessfalling weaknessfatigue weaknessn\aloc weaknessper weaknesss weaknesssob weaknesssore weaknessunstable weaknessvison weaknessx weaknss Over 50,000 unique words with no copy editing. How to clean up mistakes? 10
11 Unified Medical Language System (UMLS) Metathesaurus 10.5 million atoms in thesaurus (1.2 in SNOMED) Semantic Network 51.4 million relationships (2.9 in SNOMED) Lexicon 3.3 million spellings, inflections, properties, modifiers, abbreviations Together: 23 gigabytes of plaintext At 60 words per minute this is about 7 years of non-stop typing. 11
12 UMLS Challenges Incredible number of arbitrary decisions What should be the semantic types? Why is Mammal a semantic type but not primate What words to include? What relationships? Overly inclusive 10.5 million atoms in thesaurus(1.2 in SNOMED) Orders of magnitude more words in the thesaurus than are unique words in the 50k records How does one curate something this large? What do you do with it? 12
13 Metamap Results Example 1. Pt here with c/o N/V and "shakes" 2. decreased po intake x 2 day 3. pt has pain pump which has been out of medication x 1 week 4. pt now taking PO narcotics 5. but presenting with N/V 6. pt with chronic back and neck pain 1. Tremor [Sign or Symptom] 2. Decreased [Quantitative Concept], Oral [Spatial Concept], /day [Temporal Concept] 3. Pain [Sign or Symptom], Pump, device [Medical Device], Drugs [Pharmacologic Substance], week [Temporal Concept] 4. Take [Health Care Activity], Oral [Spatial Concept] 5. Presentation [Idea or Concept], N+ (tumor staging) [Intellectual Product] 6. Chronic [Temporal Concept], Neck pain [Sign or Symptom] 13
14 Patients with Chest Pain 14
15 Chest Pain 15
16 Fever / Febrile Seizure 16
17 Pharmacovigilance: Patients with Diabetes Blood pressure Dementia Schizophrenia Diuretic 17
18 Some other associations Drug Indication Patient cluster Tussionex Opioid TIA Altace Blood pressure Chest pain Metformin Blood sugar Nose bleed Tylenol Analgesic Rabies Buspirone Anxiolytic Dog/cat bite Cassodex Chemo Sickle cell Vasotec Blood pressure Nose bleed Prednisolone Inflammation Eye pain Wellbutrin Depression Nose bleed Zomig Migraine Chest pain 18
19 Models for comparison Use UMLS to process RN notes Ignore UMLS and just throw out rare words from the RN notes Cluster with chief complaint Cluster with MetaMAP only 19
20 Validation of Associations Train the model on 20,000 samples selected uniformly at random Test for association between patient clusters and drugs Fit the model to the test data Model using MetaMap 4% of drug symptom association tests have p-value<.05 (3021) 56% validate Model using terms in RN notes 2% of tests significant (2258) 58% validate Model using chief complaint 3% of tests significant (9381) 18% validate Model using MetaMap concepts only 3% of tests significant (9319) 22% validate 20
21 Differential Diagnosis Average correlation (nonparametric) Model with metamap,.61 Model with RN notes,.62 Chief complaint as classifier,.26 21
22 Identify New Patient Populations 1.3% of patients. 22
23 Other possible groups to add Minor trauma requiring x-ray, cast, splint 13% of patients Chronic pain, headache, sickle cell crisis 5.5% Cellulitis, local infection 4% >50% of patients on either hydroxyurea or procrit presented with sickle cell crisis 23
24 ID Patients by Drug Ritonavir Hydroxyurea Procrit Levodopa metabolic imbalance (kidney disease) sickle cell sickle cell Altered mental status 24
25 Disease Processes over Time Learn a parametric model that describes the likelihood of observing particular diseases through time Probability of disease over time y = disease severity log(y) = -a(d 2 +x 2 ) 1/2 +bx a and b give control of rate of change of disease severity d gives control of peak width Additional parameters allow control of height and temporal shift Rhinovirus Time influenza Combination of clustering model and temporal disease severity model allows identification and tracking of disease through time. 25
26 Disease Trajectory New Data set: 3.5 million patients, 7 years Why is it important to predict future disease Identification of high risk patients for early intervention Early intervention might be: Standard therapy Health coaching (telephone or in person) Enrollment in clinical trial Trajectory of a patient population with Alzheimer s Early signs that patient is developing Alzheimer s disease 26
27 Utilize all types of data to track and predict important features in the evolution of a patient s disease. 27
28 Acknowledgments Quintiles Payer / Provider Brian Kelly Jon Morris Duke University Ricardo Henao Larry Carin Geoff Ginsburg joseph.lucas@quintiles.com joe@stat.duke.edu 28
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