ArticleNPJ digital medicine2024
Privacy-preserving large language models for structured medical information retrieval.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed.
- Pneumatic retinopexy identification in electronic medical records using an on-premises large language model.Eye (London, England) · 2026Article
- SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.Computer methods and programs in biomedicine · 2026Article
- Large Language Models for Clinical Data Extraction in Workplace Injury Rehabilitation: Protocol for a Retrospective Pilot Study of Accuracy, Fairness, and Methodological Considerations in Workers' Compensation Medical Chart Review.JMIR research protocols · 2026Article
- Computational Approaches to Penicillin Allergy De-labelling: A Clinical Review.Clinical reviews in allergy & immunology · 2026Review
- Associations Between Systemic Diseases and Dental Pulp Diagnoses: A Retrospective Chart Study Using a Locally Deployed Large Language Model.Dentistry journal · 2026Article
- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- Guideline-augmented large language models for contraindication screening in interventional spine care.Interventional pain medicine · 2026Article
- Automating the Management of Extraspinal Findings in Magnetic Resonance Imaging Spine Studies Using a Privacy-Preserving Large Language Model: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Testing the use of local large language models to extract trauma identification and contextualize posttraumatic stress symptoms from self-report.Journal of behavioral medicine · 2026Article
- Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- From API to Action: A Multi-Model Comparison of OpenAI, Anthropic, Google, and Meta LLMs for Clinical Trial Data Extraction.Bioengineering (Basel, Switzerland) · 2026Article
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Review
- Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology.NPJ precision oncology · 2026Article
- Detection of Self-Harm in Electronic Mental Health Records Using Privacy-Preserving Local Language Models: Methodological Study.JMIR mental health · 2026Article
- Open-source large language model-based on-premises pipeline for automated data extraction from unstructured electronic health records: a pilot study.BMJ health & care informatics · 2026Article
- Implementing a Resource-Light and Low-Code Large Language Model System for Information Extraction from Mammography Reports: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating reasoning models for therapy recommendations in gastrointestinal stromal tumors: expert and LLM-based evaluations of OpenAI o1 and DeepSeek-R1.Journal of cancer research and clinical oncology · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Most clinical information is encoded as free text, not accessible for quantitative analysis. This study presents an open-source pipeline using the local large language model (LLM) "Llama 2" to extract quantitative information from clinical text and evaluates its performance in identifying features of decompensated liver cirrhosis. The LLM identified five key clinical features in a zero- and one-shot manner from 500 patient medical histories in the MIMIC IV dataset. We compared LLMs of three sizes and various prompt engineering approaches, with predictions compared against ground truth from three blinded medical experts. Our pipeline achieved high accuracy, detecting liver cirrhosis with 100% sensitivity and 96% specificity. High sensitivities and specificities were also yielded for detecting ascites (95%, 95%), confusion (76%, 94%), abdominal pain (84%, 97%), and shortness of breath (87%, 97%) using the 70 billion parameter model, which outperformed smaller versions. Our study successfully demonstrates the capability of locally deployed LLMs to extract clinical information from free text with low hardware requirements.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.