Can Physician Expertise Improve Machine Learning Identification of Delirium?
Delirium is common in hospitalized patients and is often missed in routine care. We present a user-centered interactive machine learning (UC-iML) framework for delirium detection support that combines physician-guided feature refinement with interpretable modeling. Using 3,862 labeled admissions from six Toronto hospitals in the General Medicine Inpatient Initiative (GEMINI), we integrate administrative variables, laboratory results, medications, and a radiology-derived text indicator. Physicians guide feature refinement and model evaluation, and Shapley Additive exPlanations (SHAP) are used to summarize feature attribution. We evaluate standard supervised classifiers with temporally sepa...
| Authors | Xinyu Qin, Vicky Ye, Ruiheng Yu et al. |
|---|---|
| Primary category | cs.CY |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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