Symptom Recognition in Medical Conversations Via multi- Instance Learning and Prompt.
With the widespread adoption of electronic health record (EHR) systems, there is a crucial need for automatic extraction of key symptom information from medical dialogue to support intelligent medical record generation. However, symptom recognition in such dialogues remains challenging because (a) s...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 14 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
8/20/2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187434316&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187434316 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/20/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187434316 187434316 187434316 10.1007/s10916-025-02240-w 187434316 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Symptom Recognition in Medical Conversations Via multi- Instance Learning and Prompt. aug: au: Wang, Hua Bai, Xue-Feng Cui, Xiu-Tao Chen, Gang Fan, Guo-Ming Wei, Guo-Lian Zheng, Ye-Ping Wu, Jing-Jing Gao, Sheng-Sheng affil: https://ror.org/006teas31 School of Mechatronic Engineering and Automation, Shanghai University, 200444, Shanghai, China sug: subj: Conversation Natural Language Processing Machine Learning Electronic Health Records Human Funding Source Descriptive Statistics Comparative Studies ab: With the widespread adoption of electronic health record (EHR) systems, there is a crucial need for automatic extraction of key symptom information from medical dialogue to support intelligent medical record generation. However, symptom recognition in such dialogues remains challenging because (a) symptom clues are scattered across multi-turn, unstructured conversations, (b) patient descriptions are often informal and deviate from standardized terminology, and (c) many symptom statements are ambiguous or negated, making them difficult for conventional models to interpret. To address these challenges, we propose a novel symptom identification approach that combines multi-instance learning (MIL) with prompt-guided attention for fine-grained symptom identification. In our framework, each conversation is treated as a bag of utterances. A MIL-based model aggregates information across utterances to improve recall and pinpoints which specific utterances mention each symptom, thus enabling sentence-level symptom recognition. Concurrently, a prompt-guided attention strategy leverages standardized symptom terminology as prior knowledge to guide the model in recognizing synonyms, implicit symptom mentions, and negations, thereby improving precision. We further employ R-Drop regularization to enhance robustness against noisy inputs. Experiments on public medical-dialogue datasets demonstrate that our method significantly outperforms existing techniques, achieving an 85.93% F1-score (with 85.09% precision and 86.83% recall) — about 8% points higher than a strong multi-label classification baseline. Notably, our model accurately identifies the specific utterances corresponding to each symptom mention (symptom–utterance pairs), highlighting its fine-grained extraction capability. Ablation studies confirm that the MIL component boosts recall, while the prompt-guided attention component reduces false positives. By precisely locating symptom information within conversations, our approach effectively tackles the issues of dispersed data and inconsistent expressions. This fine-grained symptom documentation capability represents a promising advancement for automated medical information extraction, more intelligent EHR systems, and diagnostic decision support. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|