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Rsqrd AI: Application of Explanation Model in Healthcare

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Explainable AI in Healthcare
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Rsqrd AI: Application of Explanation Model in Healthcare

  1. 1. Jasmine Wilkerson jasminewilkerson@gmail.com Application of Explanation Model In Healthcare
  2. 2. Agenda • Why Explanation Model • Which Explanation models • Internal vs external application
  3. 3. Interpretable machine learning • Explanations of AI/machine learning models to humans with domain knowledge [Craik 1967, Doshi-Velez 2014] • Why is the prediction being made? • Comprehensible to humans in • (i) natural language • (ii) easy to understand representations
  4. 4. Why do we need explanation model? • When fairness is critical: • Any context where humans are required to provide explanations so that people cannot hide behind machine learning models [Al-Shedivat 2017B, Doshi-Velez 2014] • When consequences are far-reaching: • Predictions can have far reaching consequences e.g., recommend an operation, recommend sending a patient to hospice etc. • When the cost of a mistake is high: • Ex: misclassification of a malignant tumor can be costly and dangerous • When a new/unknown hypothesis is drawn: • “It's not a human move. I've never seen a human play this move.” [Fan Hui] • Pneumonia patients with asthma had lower risk of dying [Caruana 2015]
  5. 5. Why do we need explanation model? Well, the computer assures me that you are fine and it has given me 100 reasons for it. Doctor, are you sure that I am ok now? My head still hurts.
  6. 6. Why do we need explanation model? • Understanding end-to-end application criteria o Who are the end users o Scoring scenarios o Engineering requirements o Model Properties Explanation Model Local (n=1) Global (n=N) Explanation Type Model Specificity LIME X Relative Importance Agnostic SHAP Kernel Explainer X X Relative Importance Agnostic SHAP Tree Explainer X X Relative Importance Tree Based Models GAM X Graphical Self GA2M X Graphical Self ICE Plots X Graphical Agnostic Partial Dependence Plots X Graphical Agnostic Model Distillation X Graphical Agnostic Logistic Regression X Relative Importance Self Decision Trees X Rules Self XGB Explainer X Relative Importance XGBoost
  7. 7. Explanation Model Evaluation Criteria Performance • What is the acceptable tolerance of model performance (Precision, Recall, MAE, AUC etc) Scalability • Size of features • Scoring Time • Size of training set Model Composition • Ante-hoc: explanation are part of the models themselves • Post-hoc: Explanations are generated by other techniques from the predictive model
  8. 8. Explanation Model Evaluation Criteria Model Explanation Fidelity • Does the explanation correspond to how the model is making the prediction Model Specificity • Is the explanation model specific to a particular ML model or can it be used with all models Risk What is the risk associated with the outcome that is being predicted e.g., predicting end of life incorrectly has a much higher associated risk as compared to predicting pharmacy cost
  9. 9. Explanation Model Evaluation Criteria
  10. 10. Use cases Data MIMIC III ICU Features • Demographic • Procedure • Diagnosis • Vital • Lab • Utilization Prediction Model Xgboost Explanation Model Shapley Value Problem Statement: Predict Length Of Stay of ICU encounters
  11. 11. Shapley Global Explanation - LOS
  12. 12. Shapley Global Explanation Correlation - LOS
  13. 13. Shapley Global Explanation Features Clusters - LOS
  14. 14. Shapley Local Explanation Patient 1 Predicted LOS : 2.04 Actual LOS : 2.35 Patient 2 Predicted LOS : 13.21 Actual LOS : 15.21 Coronary atherosclerosis and other heart disease Acute and unspecified renal failure Congestive heart failure; nonhypertensive Essential hypertension Respiratory failure; insufficiency; arrest (ad... Cardiac arrest and ventricular fibrillation Acute myocardial infarction Pneumonia (except that caused by tuberculosis ... Congestive heart failure; non-hypertensive Epilepsy; convulsions Coma; stupor; and brain damage Peripheral and visceral atherosclerosis Respiratory failure; insufficiency; arrest (ad... Hepatitis Diverticulosis and diverticulitis
  15. 15. "We don't believe in artificial intelligence. We believe in assistive intelligence. We leave the decision on how to act in the hands of well-trained experts, like the physicians.” says Dr. Ankur Teredesai, CTO, KenSci Inc.
  16. 16. Thanks !!

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