arXiv:2606.30651v1 Announce Type: cross Abstract: 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, a
The increasing availability of healthcare data and advanced machine learning techniques, combined with a growing emphasis on explainable AI in medicine, enables this integration.
This development represents a significant step towards more reliable and interpretable AI in critical medical diagnosis, potentially improving patient outcomes and reducing healthcare costs.
The ability to integrate physician expertise directly into the machine learning loop changes how healthcare AI models are built, shifting towards more collaborative and human-centric designs.
- · Healthcare systems
- · Medical AI companies
- · Patients
- · Physicians
- · Traditional diagnostic methods (potentially)
- · AI models lacking interpretability
Physicians will gain a new tool that significantly enhances their ability to diagnose and manage delirium.
The successful application of user-centered interactive machine learning in delirium detection could accelerate its adoption across other complex medical conditions.
This could lead to a restructuring of medical education, emphasizing human-AI collaboration and interpretability skills for future clinicians.
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Read at arXiv cs.LG