# Optimal Stopping in Sequential Clinical Prediction

arXiv: https://arxiv.org/abs/2604.22216 · HTML: https://mlsanity.com/paper/2604.22216/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [Safe to Stop? Risk-Constrained Stopping for Sequential Clinical Diagnosis Agents](https://mlsanity.com/paper/2609.09678/) (2026)

## Abstract (arXiv)

Most clinical prediction studies are developed from retrospective cohorts and reported as if all patient information were observed at once. In practice, clinicians face a more consequential question: when is there already enough information to stop testing and act? A later stage can produce a better-looking model and still fail to justify the added delay, burden, or invasiveness of further workup. We formulate sequential clinical prediction as an optimal-stopping problem under staged information, and illustrate the framework across four retrospective clinical datasets. The preferred stopping stage differed substantially by setting: sometimes fuller information justified waiting, whereas in other cases early or intermediate action was preferable. The key object is the patient-specific conditional risk trajectory: forward martingale structure represents coherent risk updating across stages, while reverse-martingale ideas describe information loss when a richer predictor is replaced by a simpler score. The results demonstrate that the best-performing model is not always the best stage for clinical decision-making.