RapidNeuroGuide: an NLP-enhanced AI platform for digital headache triage and clinical decision support
BMC Emergency Medicine, cilt.26, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s12873-026-01730-5
- Dergi Adı: BMC Emergency Medicine
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Headache triage, Artificial intelligence, Natural language processing, Emergency medicine, Biomedical language models
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Background: Headache is among the most frequent presenting complaints in emergency departments worldwide, yet differentiating life-threatening secondary causes from benign primary headaches remains a persistent clinical challenge. Objective: This study describes Phase I of the RapidNeuroGuide development roadmap, comprising the development and an initial literature-derived technical proof-of-concept evaluation of a three-layer AI-driven headache triage engine that integrates critical threshold detection, SNNOOP10 red-flag scoring, and expert-weighted risk assessment. Methods: A total of 240 PubMed-indexed headache articles were screened, and 121 full-text documents were acquired from legally accessible sources. The S-PubMedBERT-MS-MARCO sentence-transformer model was used to identify clinically relevant sentences, which were mapped to 23 structured clinical fields using a deterministic terminology dictionary comprising 103 expressions. The resulting 158 curated, literature-derived case representations constituted the proof-of-concept evaluation dataset. Results: Exact agreement with the predefined reference triage classification was observed in 110 of 158 case representations (69.6%), while 157 cases (99.4%) were assigned within one adjacent triage category. One case (0.6%) differed by more than one category. All 47 case representations carrying a predefined CRITICAL reference classification were assigned to the CRITICAL category within the evaluated dataset. The NLP pipeline produced usable structured parameter sets for 114 of 119 machine-readable documents (95.8%), with a mean cosine similarity of 0.878 across 570 retained sentences. Conclusions: RapidNeuroGuide demonstrated preliminary technical feasibility and measurable agreement with predefined reference triage categories in this Phase I proof-of-concept evaluation.