Author(s): Marie Humbert-Droz, PhD

Originally published on Towards AI.

Kicking off a hands-on series on building explainable AI models for healthcare

In my last series, we tackled a critical question: How do we detect hallucinations in large language models built for clinical use?

This article discusses the importance of explainability in healthcare AI, specifically focusing on building an explainable machine learning pipeline for hospital readmission prediction using MIMIC-IV data. The author plans to address the black box problem of lack of transparency in predictive modeling and emphasizes the need for clinicians to understand model reasoning. Throughout the series, the intention is to develop a transparent model that not only predicts readmissions but also elucidates the decision-making processes involved, ensuring the model’s clinical relevance and compliance with healthcare standards.

Read the full blog for free on Medium.

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Published via Towards AI