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Computational Drug Repositioning by Machine Learning from EHR Data
David Page Center for Predictive Computational Phenotyping University of Wisconsin-Madison
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EHR Data in a Relational Data Warehouse
Demographics Diagnoses Lab Results On the second day the knee was better, and on the third day it disappeared; The patient has been depressed since she began seeing me in 1993; Discharge status: Alive but without my permission; The patient refused autopsy; Patient’s medical history has been remarkably insignificant with only a 40 pound weight gain in the past three days; She is numb from her toes down; The skin was moist and dry; Occasional, constant infrequent headaches; Patient was alert and unresponsive; Rectal examination revealed a normal size thyroid; I saw your patient today, who is still under our car for physical therapy; Skin: somewhat pale but present; Patient was seen in consultation by Dr. Blank, who felt we should sit on the abdomen and I agree; Large brown stool ambulating in the hall; Patient has two teenage children, but no other abnormalities. Vitals Medications
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Alternative View of Patient Data: Irregularly-Sampled Time Series
Ddx: GERD, angina, costochondritis, aortic dissection, pericarditis, pulmonary embolism, pneumothorax
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But Most ML Algorithms Expect:
Single Table (Spreadsheet), or Regularly-Sampled Time Series Another Challenge: Most ML algorithms aim at accurate prediction, not causal discovery, and many potential confounders are unmeasured, and possibly not even considered
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High-Throughput ML (Kleiman, Bennett, et al., 2016)
Predicting Every ICD Diagnosis Code at the Press of a Button
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Extending SCCS to Numerical Response (Kuang et al.)
Electronic Health Records (EHRs) Properties Person 1 Longitudinal 200 160 150 Observational Diabetes 120 Applications Drug 1 Drug 2 Age 30 60 Adverse Drug Person 2 Reaction (ADR) discovery Computational Drug Bleeding Repositioning (CDR) Drug 2 Age
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A Critical Intuition: Underlying Baseline
Person 1 Diabetes Drug 2 Age 30 60 Person 2 Bleeding Age 30 60 Baseline: Blood sugar level under no influence of any drugs.
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Fixed Effect Model Fixed Effect Model (Frees, 2004): yij | xij
Person 1 Diabetes Insulin Insulin Age 30 45 60 Fixed Effect Model (Frees, 2004): yij | xij = αi + β⊤xij + ϵij, ϵij ∼ N(0,σ2). dimβ =# drugs
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Time-Varying Baseline
Person 1 Diabetes Insulin Insulin Age 30 45 60 Time-Varying Baseline, add regularization to minimize change in proximal, consecutive tij values: yij | xij = tij + β⊤xij + ϵij, ϵij ∼ N(0,σ2).
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Time-Varying Baseline
Person 1 Diabetes Insulin Insulin Age 30 45 60 Time-Varying Baseline: add regularization to minimize change in proximal, consecutive tij values: yij | xij = tij + β⊤xij + ϵij, ϵij ∼ N(0,σ2). Last term penalizes consecutive baseline values that are close together in time but far apart in numerical value.
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Ground Truth for Drugs that are Glucose Lowering
1.00 1.00 Methods ABR CSCCSA 0.75 0.75 BR CSCCS 0.50 0.50 Methods ABR 0.25 0.25 CSCCSA BR CSCCS 0.00 0.00 K FPR Threshold Figure: Left: Precision at K among the top-forty drugs generated by the four models; Right: Partial AUCs on the top-forty drugs generated by the four models. Sample size: Number of drug candidates: 2980.
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Recovery of Known Glucose Lowering Agents
INDX CODE DRUG NAME SCORE COUNT INDX CODE DRUG NAME SCORE COUNT 1 4485 HUMALOG 124 1 4802 INSULIN 47 635 2 7470 PIOGLITAZONE HCL 3075 2 8316 REZULIN 50 120 3 8437 ROSIGLITAZONE MALEATE -9.731 1019 3 824 AVANDIA 59 449 4 4837 INSULN ASP PRT/INSULIN ASPART -9.658 258 4 416 AMARYL 65 503 5 6382 NEEDLES INSULIN DISPOSABLE -9.464 2827 5 5226 LANTUS 66 33 6 4171 GLUCOTROL XL -8.117 2853 6 5789 METFORMIN HYDROCHLORIDE 75 10 7 4106 GLIMEPIRIDE -7.940 3384 7 4485 HUMALOG 81 63 8 160 ACTOS -7.721 1125 8 4132 GLUCOPHAGE 86 1813 9 824 AVANDIA -6.802 1239 9 4811 INSULIN NPH 88 19 10 9152 SYRING W-NDL DISP INSUL 0.5ML -6.623 4186 10 144 ACTIGALL 90 34 11 4132 GLUCOPHAGE -6.322 6736 11 1389 CAL 90 45 12 4184 GLYBURIDE -6.021 8879 12 4171 GLUCOTROL XL 90 701 13 4170 GLUCOTROL -5.721 1259 13 9155 SYRNG W-NDL DISP INSUL 0.333ML 95 29 14 4208 GLYNASE -5.670 591 14 4116 GLUCAGON 97 121 15 416 AMARYL -5.599 2240 15 6652 NOVOLOG 98 51 16 4107 GLIPIZIDE -5.563 9993 16 160 ACTOS 106 480 17 844 AXID -4.682 189 17 6646 NOVOFINE 31 106 31 18 2830 DILTIAZEM -4.297 1021 18 4813 INSULIN NPL/INSULIN LISPRO 108 118 19 4806 INSULIN GLARGINE HUM.REC.ANLOG -4.175 4213 19 8437 ROSIGLITAZONE MALEATE 109 332 20 5787 METFORMIN HCL -4.147 19584 20 4170 GLUCOTROL 113 641 21 2824 DILAUDID -4.076 39 21 9889 URSODIOL 113 123 22 5786 METFORMIN -3.890 3838 22 5052 KAY CIEL 114 23 23 7731 PRAVACHOL -3.532 1700 23 4118 GLUCAGON HUMAN RECOMBINANT 115 227 24 1760 CELEXA -3.517 1473 24 2521 DARVOCET-N 116 11 25 4497 HUM INSULIN NPH/REG INSULIN HM -3.501 1829 25 7470 PIOGLITAZONE HCL 121 705 26 9889 URSODIOL -3.132 376 26 5786 METFORMIN 125 2149 27 4813 INSULIN NPL/INSULIN LISPRO -2.972 623 27 10366 ZINC SULFATE 130 34 28 4133 GLUCOPHAGE XR -2.845 765 28 4500 HUMULIN 135 33 29 6445 NEURONTIN -2.615 1418 29 4172 GLUCOVANCE 136 115 30 6656 NPH HUMAN INSULIN ISOPHANE -2.500 2874 30 7471 PIOGLITAZONE HCL/METFORMIN HCL 136 16 31 9379 THIAMINE HCL -2.383 341 31 6382 NEEDLES INSULIN DISPOSABLE 137 649 32 1636 CARDURA -2.198 1079 32 4184 GLYBURIDE 144 1354 33 1218 BLOOD SUGAR DIAGNOSTIC DRUM -2.073 2593 33 4208 GLYNASE 145 115 34 8025 PROZAC -2.037 1525 34 4210 GLYSET 148 7 35 8316 REZULIN -1.895 444 35 4163 GLUCOSE 159 1778 36 9136 SYRINGE & NEEDLE INSULIN 1 ML -1.885 3542 36 5977 MINIPRESS 163 19 37 4802 INSULIN -1.812 1526 37 7946 PROPANTHELINE 163 6 38 7674 POTASSIUM CHLORIDE -1.779 9842 38 1602 CARBOCAINE 182 63 39 4804 INSULIN ASPART -1.752 2476 39 1305 BUDEPRION SR 185 9 40 1200 BLOOD-GLUCOSE METER -1.719 5289 40 6657 NPH INSULIN 185 43
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Conclusion Impossible to perfectly infer causation from purely observational data such as EHR But real applications are often about causation Emprical results suggest can do this well for some drugs and some frequent lab measurements Many other potential applications Treatment response and adverse drug events Other biological tasks Other causal discovery tasks
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Thanks! NIH BD2K Program NLM, NIGMS, NIAID Charles Kuang Peggy Peissig
Michael Caldwell James Thomson Ron Stewart
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