Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation
This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framew...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8412 |
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| author | Alper Karamanlıoğlu Berkan Demirel Onur Tural Osman Tufan Doğan Ferda Nur Alpaslan |
| author_facet | Alper Karamanlıoğlu Berkan Demirel Onur Tural Osman Tufan Doğan Ferda Nur Alpaslan |
| author_sort | Alper Karamanlıoğlu |
| collection | DOAJ |
| description | This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framework, the system addresses key deployment challenges in high-stakes clinical settings. Unlike traditional models, the architecture processes both structured (vitals, labs) and unstructured (clinical notes) data to enable context-aware reasoning with clinically acceptable latency at the point of care. It leverages big data infrastructure for large-scale EHR management and incorporates digital twin concepts for live patient monitoring. Federated training allows institutions to collaboratively improve models without sharing raw data, ensuring compliance with GDPR/HIPAA, and FAIR principles. Privacy is further protected through differential privacy, secure aggregation, and inference isolation. We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (<i>n</i> = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. The system demonstrated clinically acceptable latency and promising alignment with expert judgment on reviewed cases. The infectious disease triage case demonstrates low-latency recognition of sepsis-like presentations in the ED. This work offers a scalable, audit-compliant, and clinician-validated blueprint for CDSS, enabling low-latency triage and extensibility across specialties. |
| format | Article |
| id | doaj-art-b47cfd2cf12f427788c173e8208eacfc |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b47cfd2cf12f427788c173e8208eacfc2025-08-20T03:36:02ZengMDPI AGApplied Sciences2076-34172025-07-011515841210.3390/app15158412Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World EvaluationAlper Karamanlıoğlu0Berkan Demirel1Onur Tural2Osman Tufan Doğan3Ferda Nur Alpaslan4Department of Computer Engineering, Middle East Technical University, Ankara 06800, TürkiyeProduct and R&D, TURKSAT, Ankara 06839, TürkiyeEmergency Medicine, Zonguldak Bülent Ecevit University, Zonguldak 67100, TürkiyeR&D and Innovation, Innova IT Solutions, Ankara 06800, TürkiyeDepartment of Computer Engineering, Middle East Technical University, Ankara 06800, TürkiyeThis study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framework, the system addresses key deployment challenges in high-stakes clinical settings. Unlike traditional models, the architecture processes both structured (vitals, labs) and unstructured (clinical notes) data to enable context-aware reasoning with clinically acceptable latency at the point of care. It leverages big data infrastructure for large-scale EHR management and incorporates digital twin concepts for live patient monitoring. Federated training allows institutions to collaboratively improve models without sharing raw data, ensuring compliance with GDPR/HIPAA, and FAIR principles. Privacy is further protected through differential privacy, secure aggregation, and inference isolation. We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (<i>n</i> = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. The system demonstrated clinically acceptable latency and promising alignment with expert judgment on reviewed cases. The infectious disease triage case demonstrates low-latency recognition of sepsis-like presentations in the ED. This work offers a scalable, audit-compliant, and clinician-validated blueprint for CDSS, enabling low-latency triage and extensibility across specialties.https://www.mdpi.com/2076-3417/15/15/8412clinical decision supporttriagefair data principlesgenerative AIlarge language modelsfederated learning |
| spellingShingle | Alper Karamanlıoğlu Berkan Demirel Onur Tural Osman Tufan Doğan Ferda Nur Alpaslan Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation Applied Sciences clinical decision support triage fair data principles generative AI large language models federated learning |
| title | Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation |
| title_full | Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation |
| title_fullStr | Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation |
| title_full_unstemmed | Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation |
| title_short | Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation |
| title_sort | privacy preserving clinical decision support for emergency triage using llms system architecture and real world evaluation |
| topic | clinical decision support triage fair data principles generative AI large language models federated learning |
| url | https://www.mdpi.com/2076-3417/15/15/8412 |
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