SOAPTriage: SOAP-Guided Multi-View Clinical Text Modeling Framework for Automated ESI Prediction
Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu
摘要
Emergency departments (ED) rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care, a process that is largely driven by clinical triage text. Despite recent progress in automated ESI prediction, two fundamental challenges remain: the scarcity of high-quality triage text data due to privacy and regulatory constraints and the lack of a clinically grounded triage framework capable of explicitly capturing the multidimensional structure of triage reasoning. To address these challenges, we draw inspiration from the clinically grounded SOAP paradigm, in which SOAP refers to Subjective, Objective, Assessment, and Plan and captures four complementary aspects of clinical reasoning. Building on this paradigm, we propose SOAPTriage, a SOAPguided multi-view clinical text modeling framework for automated ESI prediction. To mitigate data scarcity, SOAPTriage introduces a Clinical Note Augmentation (CNA) module that generates natural-language triage notes from structured ED records, resulting in 15,393 augmented clinical notes derived from a realworld dataset. To incorporate clinical structure, SOAPTriage employs a SOAP-Guided Encoding (SGE) module that models patient conditions from four complementary SOAP perspectives, together with an adaptive SOAP-Aware Aggregation and Inference (SAAI) module that performs multi-view reasoning to infer ESI levels. Extensive experiments show that SOAPTriage consistently outperforms strong prompting-based, multi-agent, and encoderbased baselines, demonstrating the effectiveness of SOAP-guided multi-view clinical text modeling for automated emergency triage. 1 * Equal contribution † Corresponding author 1 Our code and datasets can be found at https://github. com/xiaoyaoiii/SOAPTriage . Gap (i): Severe scarcity of highquality triage data. Structured ED Records Real-World Clinical Notes LLM-Generated Clinical Notes 15,393 Clinical Triage Notes Gap (ii): Lack of a clinically grounded triage framework. SOAP Clinical Theory Mid-Depth Embeddings Aggregation & Inference Reliable ESI Prediction Figure 1: Overview of SOAPTriage. The framework tackles triage data scarcity by generating clinical notes from structured records and real-world notes. It also addresses the lack of a clinically grounded triage framework by incorporating SOAP-guided reasoning, extracting multi-view representations, and aggregating them to infer reliable ESI predictions.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper7
- Large Language Models are Zero-Shot ReasonersTakeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo 等NeurIPS 2022 · 被引用 8,168 次
- Self-Consistency Improves Chain of Thought Reasoning in Language ModelsXuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le 等ICLR 2023 · 被引用 681 次
- IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner MonologuesDiji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo 等SIGIR 2024 · 被引用 45 次
- Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMsDanni Liu, Jan NiehuesACL 2025 · 被引用 23 次
- Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model CommunicationZhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo 等EMNLP 2023 · 被引用 15 次
相关 Paper
- Multi-Label Few-Shot ICD Coding as Autoregressive Generation with PromptZhichao Yang, Sunjae Kwon, Zonghai Yao, Hong YuAAAI 2023 · 被引用 29 次
- Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented GenerationKyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon KimKDD 2026
- Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention GuidanceYue Fang, Yuxin Guo, Jiaran Gao, Hongxin Ding 等AAAI 2026 · 被引用 4 次
- EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context ReasoningHengyu Zhang, Xuyun Zhang, Pengxiang Zhan, Linhao Luo 等KDD 2026
- Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical NotesYang Zhou, Zhenting Sheng, Mingrui Tan, Yuting Song 等AAAI 2026
