Es#ma#ng Clinical State Variables without Labeled Data
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1 Es#ma#ng Clinical State Variables without Labeled Data Yoni Halpern Joint work with: Youngduck Choi Steven Horng MD, MMSc David Sontag, PhD
2 Disclosure The authors have no rela#onships with commercial interests.
3 Emergency Department: Limited resources Time sensi:ve Cri:cal decisions
4 Outline Use case: Real- #me phenotype es#ma#on Current Approaches manual rules, machine learning Anchor- based learning defining anchors learning framework interac#ve anchor specifica#on Evalua#on Conclusion and Next Steps
5 Real- #me phenotype es#ma#on Celluli:s Specialized order sets, confirm followup care. From nursing home Higher risk for acquired infec#ons Geriatric fall Alert transport staff for fall precau#ons Many more GI bleed, syncope, DKA, etc.
6 Real- #me phenotype es#ma#on Celluli:s Specialized order sets, confirm followup care. From nursing home Higher risk for acquired infec#ons Geriatric fall Alert transport staff for fall precau#ons Many more GI bleed, syncope, DKA, etc.
7 Real- #me phenotype es#ma#on Celluli:s Specialized order sets, confirm followup care. From nursing home Higher risk for acquired infec#ons Geriatric fall Alert transport staff for fall precau#ons Many more GI bleed, syncope, DKA, etc.
8 Representa#on To trigger effec#ve decision support, the computer needs to know: Is the pa#ent from a nursing home? Does the pa#ent have an infec#on, altered mental status, require a cardiology consult,? Hundreds of such phenotype variables that would be valuable for decision support Entering this informa#on in structured form would be a nightmare!
9 Big Picture All pa#ent observa#ons MD/nurse documenta#on Billing codes Vitals Orders Labs History Phenotype variables nsg home? AMS? cards? infec#on? Ac#on Alerts/ Reminders Decision support QA review Contextual display Cohort Selec#on
10 Big Picture All pa#ent observa#ons MD/nurse documenta#on Billing codes Vitals Orders Labs History Phenotype variables nsg home? AMS? cards? infec#on? How do we extract this representa#on from pa#ent records? Easy Structured: allergies, medica:ons, vital signs Harder Free text: smokes, domiciled, infec:on, histories Hardest Inferred: DDx, risk assessment, missing info
11 Current Approaches I: Manually created rules Nursing home: is the phrase nursing home in the pa#ent s notes? Regional list of names, addresses. Ac:ve Malignancy: Diagnosis codes, key phrases in radiology reports, Time consuming, o^en have low sensi#vity Machine learning physician response Nursing home? Logis#c regression, support vector machines, neural (gold standard) networks, decision trees Need labeled data, and some#mes T may F text contains: need to be collected prospec#vely (expensive) PPV nursing home Classifiers o^en don t generalize T across ins#tu#ons 0.70 highly dependent on representa#on trained on Need to include: nursing facility nursing care facility nursing / rehab nsg facility nsg faclty F 1, Sensi#vity 0.18
12 Current Approaches II: Machine Learning Leverage large clinical databases to learn predic#ve rules. Need labeled data Classifiers o^en don t generalize across ins#tu#ons LOINC& UMLS&CUID& RXnorm& ICD9& Unstructured&Data&
13 Our contribu#on: Learning with Anchors Use a combina#on of domain exper#se (simple rules) and vast amounts of data (machine learning). Method does not require any manual labeling. Anchors are highly transferable between ins#tu#ons.
14 What are anchors? Rather than provide gold- standard labels, construct a simple rule that can catch some posi#ve cases. Examples: Phenotype Diabe#c Cardiac Nursing home Social work Possible Anchor gsn: (insulin) in Medica#ons ICD9:428.X (heart failure) in Diagnoses from nursing home in text social work consulted in text
15 What are anchors? Rather than provide gold- standard labels, construct a simple rule that can catch some posi#ve cases. Low sensi:vity here is ok! Examples: Phenotype Diabe#c Cardiac Nursing home Social work Possible Anchor gsn: (insulin) in Medica#ons ICD9:428.X (heart failure) in Diagnoses from nursing home in text social work consulted in text
16 Theore#cal basis for anchors Unobserved variable: Y, Observa#on: A A is an anchor for Y if condi#oning on A=1 gives uniform samples from the set of posi1ve cases. Alterna#ve formula#on two necessary condi#ons: P (Y =1 A = 1) = 1 Posi:ve condi:on AND A? X Y Condi:onal independence X represents all other observa#ons.
17 Theore#cal basis for anchors Unobserved variable: Y, Observa#on: A A is an anchor for Y if condi#oning on A=1 gives uniform samples from the set of posi1ve cases. Alterna#ve formula#on two necessary condi#ons: P (Y =1 A = 1) = 1 Posi:ve condi:on e.g. If pa#ent is taking insulin, the pa#ent is surely diabe:c. AND A? X Y Condi:onal independence e.g. If we know the pa#ent had heart failure, knowing whether X represents the diagnosis all other code observa#ons. appears does inform us about the rest of the record.
18 Theore#cal basis for anchors Unobserved variable: Y, Observa#on: A A is an anchor for Y if condi#oning on A=1 gives uniform samples from the set of posi1ve cases. Theorem [Elkan & Noto 2008]: In the above se:ng, a func1on to predict A can be transformed to predict Y!
19 Learning with Anchors Pa#ent database LOINC& UMLS&CUID& RXnorm& ICD9& Unstructured&Data& Iden#fy anchors Learn to predict the anchors (anchor as pseudo- labels) Account for the difference between anchors and labels Transform Predict anchor Predict label
20 Learning with anchors Input: anchor A unlabeled pa#ents Output: predic#on rule 1. Learn a calibrated classifier (e.g. logis#c regression) to predict: Pr(A =1 X ) 2. Using a validate set, let P be the pa#ents with A=1. Compute: C = 1 X Pr(A =1 X (k) ) P k2p 3. For a previously unseen pa#ent t, predict: 1 C Pr(A =1 X (t) ) if A (t) =0 1 if A (t) =1 [Elkan & Noto 2008] Learning Learn to predict A from the other variables. Calibra:on C is the average model predic#on for pa#ents with anchors. Transforma:on If no anchor present, according to a scaled version of the anchor- predic#on model.
21 Generalizability/Portability New ins#tu#on LOINC& UMLS&CUID& RXnorm& ICD9& Different&data&types&
22 Generalizability/Portability LOINC& UMLS&CUID& RXnorm& ICD9& Different&data&types& New ins#tu#on Data may be very different: Language Representa#on Popula#on
23 Generalizability/Portability New ins#tu#on LOINC& UMLS&CUID& RXnorm& ICD9& Different&data&types& As long as our anchors appear in the new data as well
24 Generalizability/Portability New ins#tu#on LOINC& UMLS&CUID& RXnorm& ICD9& Different&data&types& As long as our anchors appear in the new data as well Can learn a new model, specific to the new ins#tu#on.
25 Generalizability/Portability New ins#tu#on LOINC& UMLS&CUID& RXnorm& ICD9& Different&data&types& As long as our anchors appear in the new data as well Can learn a new model, specific to the new ins#tu#on. Only need to share anchor defini:ons, Each site trains models on its own data.
26 Anchor Explorer V1 Specified anchors Automated sugges#ons Detailed pa#ent display Pa#ent filters Ranked pa#ent list Code freely available clinicalml.org
27 Anchor Explorer V1 Specified anchors Automated sugges#ons Rapid itera:on ~30 min to add a new phenotype Detailed pa#ent display Pa#ent filters Ranked pa#ent list Code freely available clinicalml.org
28 Evalua#on 273,174 emergency department pa#ents from Beth Israel Deaconess ED Observa#ons: Structured: age, sex, ICD9 codes*, medrecon, pyxis Preprocessed free text: chief complaint, triage assessment, and physician s comments Anchors specified by a single ED physician using our interface. *Diagnosis codes available for training but not at test #me (real #me decision support seung).
29 Test variables: ED red flags Ac#ve malignancy Fall Cardiac E#ology Infec#on From Nursing Home An#coagulated Immunosuppressed Sep#c Shock Pneumonia
30 Learned models: Nursing Home nursing facility nursing home nsg facility nsg home nsg. home Anchors Condi#onal independence assump#on? Ages age=90+ age=80-90 age=70-80 baseline changes nonverbal ams unwitnessed_fall confusion Highly weighted features Medica:ons senna mirtazapine colace maalox trazodone tums from staff at resident sent reported Pyxis vancomycin levofloxacin dnr full code g tube foley nh Unstructured text
31 Learned models: Cardiac E#ology Anchors ICD9 codes 410.* acute MI 411.* other acute 413.* angina pectoris card. shock Pyxis coron. vasodilators loop diure#c cardiac medicine BIDMC shoryorm Ages age=80-90 age=70-80 age=90+ nstemi stemi ntg lasix nitro Highly weighted features Medica:ons lasix furosemide cp chest pain edema cmed chf exacerba#on sob pedal edema Sex=M Pyxis aspirin clopidogrel Heparin Sodium Metoprolol Tartrate Morphine Sulfate cmed Integrilin Labetalol Unstructured text
32 Comparison to Exis#ng Approaches (Rules) Predict just according to the anchors. 1 if anchor is present, 0 otherwise (ML) Machine learning (logis#c regression) Using up to 3K labels Improves with more labels, but labels are expensive!
33 Phenotype predic#ons *
34 Phenotype predic#ons *
35 Phenotype predic#ons * *
36 Our next steps Shared library of anchored phenotypes Real- #me es#ma#on of clinical states and actual use for decision support within ED Test portability of anchors to other ins#tu#ons General cohort selec#on tool More info: clinicalml.org
37 Our next steps Shared library of anchored phenotypes Real- #me es#ma#on of clinical states and actual use for decision support within ED Test portability of anchors to other ins#tu#ons General cohort selec#on tool More info: clinicalml.org
38 Our next steps Shared library of anchored phenotypes Real- #me es#ma#on of clinical states and actual use for decision support within ED Test portability of anchors to other ins#tu#ons General cohort selec#on tool More info: clinicalml.org
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