{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:55:08Z","timestamp":1790974508495,"version":"4.1.0"},"reference-count":47,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72574133"],"award-info":[{"award-number":["72574133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72204153"],"award-info":[{"award-number":["72204153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>\n                    Patient-generated text in internet hospitals represents a massive but unstructured source of clinical demand. Misalignment between rigid administrative menus and actual patient intent leads to triage inefficiency and resource mismatches. We developed a hybrid artificial intelligence framework to decode patient needs and optimize service allocation using real-world data from a tertiary internet hospital, with external validation on an independent multihospital dataset (\n                    <jats:italic toggle=\"yes\">N<\/jats:italic>\n                    = 497). In this study, we analyzed 28,851 inquiries from a tertiary internet hospital using a 2-stage approach: (a) unsupervised K-Means clustering to discover intrinsic intent taxonomies, and (b) supervised classification using a fine-tuned MacBERT model. We benchmarked this specialized small model against traditional baselines and state-of-the-art large language models (LLMs; Qwen2.5 and HuatuoGPT-II) on a gold-standard dataset of 4,814 annotated records. Unsupervised analysis identified 8 distinct patient intents, revealing substantial semantic overlap between rehabilitation and follow-up requests. The fine-tuned MacBERT model achieved a weighted F1-score of 0.680, outperforming both the medical-specific LLM (F1 = 0.595) and the general LLM (F1 = 0.585). External validation on an independent nationwide platform dataset (\n                    <jats:italic toggle=\"yes\">N<\/jats:italic>\n                    = 497) confirmed cross-platform transferability, with the hybrid learning framework achieving a weighted F1 of 0.578 versus 0.389 (Qwen2.5-7B) and 0.190 (HuatuoGPT-II). Departmental analysis exposed distinct \u201cdemand fingerprints\u201d: 39% of Neurology inquiries focused on prescription renewals, whereas 68% of Dermatology inquiries involved initial symptom consultations. These findings indicate that, within the studied Chinese hospital setting, fine-tuned task-specific models may offer superior utility over general LLMs for clinical triage, with the performance gap widening under domain shift\u2014though multicenter prospective validation is required to establish generalizability. Mapping intent fingerprints across specialties provides a quantifiable basis for restructuring digital hospital operations.\n                  <\/jats:p>","DOI":"10.34133\/csbj.0132","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:32:06Z","timestamp":1779175926000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Mapping Clinical Intent in Internet Hospitals: Identifying and Profiling Patient Demand via Natural Language Processing"],"prefix":"10.34133","volume":"35","author":[{"given":"Ruoxin","family":"Xu","sequence":"first","affiliation":[{"name":"Yuebei People\u2019s Hospital Affiliated to Shantou University Medical College","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"Yuebei People\u2019s Hospital Affiliated to Shantou University Medical College","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6094-7534","authenticated-orcid":false,"given":"Huizi","family":"Ye","sequence":"additional","affiliation":[{"name":"Yuebei People\u2019s Hospital Affiliated to Shantou University Medical College","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Yang","sequence":"additional","affiliation":[{"name":"Yuebei People\u2019s Hospital Affiliated to Shantou University Medical College","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuanlie","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hainan Medical University","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Yuebei People\u2019s Hospital Affiliated to Shantou University Medical College","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","DOI":"10.2196\/47523","article-title":"Telehealth care through internet hospitals in China: Qualitative interview study of physicians\u2019 views on access, expectations, and communication","volume":"26","author":"Zhong Y","year":"2024","unstructured":"Zhong Y, Hahne J, Wang X, Wang X, Wu Y, Zhang X, Liu X. 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