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resource-constrained-machine-learning

topic2 events
papersSEP 12 04:00 UTC

arXiv Paper Proposes Active Test Selection for Timely Clinical Diagnosis

A new arXiv preprint argues that most machine learning approaches to clinical diagnosis assume fully observed, static datasets, which does not match how clinicians reason sequentially while weighing resource constraints. The authors propose a method that actively chooses which diagnostic test to order next, aiming to reach a diagnosis sooner with fewer tests. The work is presented as a replacement version of the paper (v5).

clinical-ai/active-learning/healthcare/research