Scheduling with Predictions

TL;DR

This paper introduces a learning-augmented online scheduling policy for prioritizing medical cases based on predictions of urgency, balancing performance improvements with robustness to prediction errors.

Contribution

It formulates a novel scheduling problem incorporating imperfect predictions and proposes an optimal online policy that ensures both consistency and robustness.

Findings

The proposed policy is optimal in certain stylized settings.

It achieves improved performance with better predictions while maintaining robustness.

Empirical results validate the policy's effectiveness in realistic clinical scenarios.

Abstract

There is significant interest in deploying machine learning algorithms for diagnostic radiology, as modern learning techniques have made it possible to detect abnormalities in medical images within minutes. While machine-assisted diagnoses cannot yet reliably replace human reviews of images by a radiologist, they could inform prioritization rules for determining the order by which to review patient cases so that patients with time-sensitive conditions could benefit from early intervention. We study this scenario by formulating it as a learning-augmented online scheduling problem. We are given information about each arriving patient's urgency level in advance, but these predictions are inevitably error-prone. In this formulation, we face the challenges of decision making under imperfect information, and of responding dynamically to prediction error as we observe better data in real-time. We propose a simple online policy and show that this policy is in fact the best possible in certain stylized settings. We also demonstrate that our policy achieves the two desiderata of online algorithms with predictions: consistency (performance improvement with prediction accuracy) and robustness (protection against the worst case). We complement our theoretical findings with empirical evaluations of the policy under settings that more accurately reflect clinical scenarios in the real world.