mlsanityData to 7 Oct 2026

Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis

arXiv 2203.14469 ↗First posted 28 Mar 2022cs.LG cs.AIcited by 14

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Abstract (arXiv)

Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer model for early sepsis prediction, using the physiological time series data and clinical notes for each patient within 36 hours of ICU admission. Specifically, we aim to predict sepsis using only the first 12, 18, 24, 30 and 36 hours of laboratory measurements, vital signs, patient demographics, and clinical notes. We evaluate our model on two large critical care datasets: MIMIC-III and eICU-CRD. The proposed method is compared with six baselines. In addition, ablation analysis and case studies are conducted to study the influence of each individual component of the model and the contribution of each data modality for early sepsis prediction. Experimental results demonstrate the effectiveness of our method, which outperforms competitive baselines on all metrics.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

22 of 40 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 5; 2025: 1; 2026: 2.

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  1. 2023Metacognitive Prompting Improves Understanding in Large Language Modelscited by 58
  2. 2023Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understandingcited by 40
  3. 2023TRAM: Benchmarking Temporal Reasoning for Large Language Modelscited by 39
  4. 2023Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Modelscited by 15
  5. 2024SportQA: A Benchmark for Sports Understanding in Large Language Modelscited by 15
  6. 2024RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Modelscited by 13
  7. 2023Missing-modality Enabled Multi-modal Fusion Architecture for Medical Datacited by 8
  8. 2024FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Recordscited by 6
  9. 2024Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Modelscited by 5
  10. 2025DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentationcited by 2
  11. 2026OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Predictioncited by 2
  12. 2024Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responsescited by 1
2 more
  1. 2026CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Predictioncited by 1
  2. 2023XAI for In-hospital Mortality Prediction via Multimodal ICU Datacited by 0