mlsanityData to 14 Oct 2026

CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidence

arXiv 2608.03862 ↗First posted 4 Aug 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Emergency triage requires reliable decisions within a short time period. However, the available electronic health record (EHR) data, including structured data and clinical text, are often incomplete, unreliable, and inconsistent. This makes machine learning (ML)-based triage prediction more challenging, as existing ML models typically rely on complete and reliable EHR data to accurately predict patients' acuity levels. To address this, we propose confidence- and reliability-aware selective triage (CRS-Triage) to predict patients' acuity levels with a confidence score. By comparing the confidence score with a predefined threshold, CRS-Triage can selectively determine whether the model should make the decision or defer the case. Specifically, CRS-Triage separately evaluates the reliability of structured data and clinical text and then jointly considers the consistency between the two modalities to estimate the confidence of each prediction. Moreover, to reduce the risk of missing high-acuity patients, namely under-triage, CRS-Triage prefers to assign patients slightly higher acuity levels, namely over-triage, by penalizing under-triage errors. Experiments on the MIMIC-IV-ED dataset show that CRS-Triage achieves strong predictive performance. It also provides a better risk-coverage trade-off and remains reliable when the available EHR data are incomplete, degraded, or inconsistent across modalities.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 4 Aug 2026.

Builds on

20 of 36 references.

References · 20

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  4. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  5. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  6. 2020Consistent Estimators for Learning to Defer to an Expertcited by 271
  7. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 168
  8. 2021Trusted Multi-View Classificationcited by 94
  9. 2022M3Care: Learning with Missing Modalities in Multimodal Healthcare Datacited by 72
  10. 2024DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistencycited by 53
  11. 2021Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibrationcited by 48
  12. 2022A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Predictioncited by 48
8 more
  1. 2021Role of Human-AI Interaction in Selective Predictioncited by 35
  2. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 28
  3. 2021How to Leverage Multimodal EHR Data for Better Medical Predictions?cited by 26
  4. 2023Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attentioncited by 25
  5. 2023Learning from data with structured missingnesscited by 24
  6. 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?cited by 18
  7. 2024FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Predictioncited by 6
  8. 2025Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingnesscited by 5

Built on by

Not cited yet.