Contact and support
Need help, have a question, or want to contact the ResearchHub team?
© 2026 ResearchHub. Built for responsible scholarly connection.
Privacy-aware community infrastructureResearch graph
References from The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study. Local targets link to admitted publications; unresolved targets remain external evidence.
Global burden of enteric infectious diseases, diarrhoeal diseases, and corresponding aetiologies, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
10.1016/s1473-3099(26)00194-5 · External reference
Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: a systematic review
10.1016/j.artmed.2023.102701 · External reference
Using structured codes and free-text notes to measure information complementarity in electronic health records: feasibility and validation study
10.2196/66910 · External reference
Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19
10.1002/14651858.cd013665.pub3 · External reference
Is omission of free text records a possible source of data loss and bias in Clinical Practice Research Datalink studies? A case-control study
10.1136/bmjopen-2016-011664 · External reference
A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2
10.1371/journal.pone.0323535 · External reference
irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets
10.1016/j.ebiom.2026.106227 · External reference
ZeroTuneBio NER: a three-stage framework for zero-shot and zero-tuning biomedical entity extraction using large language models and prompt engineering
10.1016/j.cmpb.2025.109070 · External reference
From real-world electronic health record data to real-world results using artificial intelligence
10.1136/ard-2022-222626 · External reference
Improving mortality prediction after radiotherapy with large language model structuring of large-scale unstructured electronic health records
10.1016/j.radonc.2025.111052 · External reference
A suite of large language models for public health infoveillance
10.1038/s41746-026-02435-6 · External reference
Synthetic data distillation enables the extraction of clinical information at scale
10.1038/s41746-025-01681-4 · External reference
Reasoning-driven large language models in medicine: opportunities, challenges, and the road ahead
10.1016/j.landig.2025.100931 · External reference
Performance of ChatGPT-4o and four open-source large language models in generating diagnoses based on China’s Rare Disease Catalog: comparative study
10.2196/69929 · External reference
Agent-based large language model system for extracting structured data from breast cancer synoptic reports: a dual-validation study
10.1093/jamiaopen/ooag016 · External reference
Unresolved reference
External reference
Promoting responsible DeepSeek deployment in health care: scoping review comparing grey and white literature
10.2196/80770 · External reference
The rise and potential opportunities of large language model agents in bioinformatics and biomedicine
10.1093/bib/bbaf601 · External reference
Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support
10.1038/s43856-025-01021-3 · External reference
The impact of temperature on extracting information from clinical trial publications using large language models
10.7759/cureus.75748 · External reference
Moving biosurveillance beyond coded data using AI for symptom detection from physician notes: retrospective cohort study
10.2196/53367 · External reference
A comparative empirical study of prompting strategies for code generation with large language models
10.69987/jacs.2025.51203 · External reference
NLD-LLM: a systematic framework for evaluating small language transformer models on natural language description
10.1109/icmla66185.2025.00227 · External reference
Optimizing app review classification with large language models: a comparative study of prompting techniques
10.1109/iceccme62383.2024.10796376 · External reference
Scalable medication extraction and discontinuation identification from electronic health records using large language models
10.1016/j.jclinepi.2025.112049 · External reference
Leveraging large language models for rare disease named entity recognition
10.1371/journal.pdig.0001242 · External reference
An empirical evaluation of prompting strategies for large language models in zero-shot clinical natural language processing: algorithm development and validation study
10.2196/55318 · External reference
Performance and improvement strategies for adapting generative large language models for electronic health record applications: a systematic review
10.1016/j.ijmedinf.2025.106091 · External reference
Verification is all you need: prompting large language models for zero-shot clinical coding
10.1109/jbhi.2025.3593028 · External reference
Accuracy of large language models to identify stroke subtypes within unstructured electronic health record data
10.1161/strokeaha.125.051993 · External reference
Information extraction from clinical notes: are we ready to switch to large language models?
10.1093/jamia/ocaf213 · External reference
Comparison of local large language models for extraction of signs and symptoms data from electronic health records
10.1371/journal.pone.0350625 · External reference
Large language model symptom identification from clinical text: multicenter study
10.2196/72984 · External reference
Scalable tracking of symptoms in the electronic health record using large language models in patients with central nervous system cancers undergoing therapy
10.1093/neuonc/noaf223 · External reference
Assessment of natural language processing of electronic health records to measure goals-of-care discussions as a clinical trial outcome
10.1001/jamanetworkopen.2023.1204 · External reference
Holistic evaluation of large language models for medical tasks with MedHELM
10.1038/s41591-025-04151-2 · External reference
From system 1 to system 2: a survey of reasoning large language models
10.1109/tpami.2025.3637037 · External reference
A software pipeline for medical information extraction with large language models, open source and suitable for oncology
10.1038/s41698-025-01103-4 · External reference
Integrating a large language model to streamline nursing handover documentation across multiple hospitals in Taiwan: development and implementation study
10.2196/81604 · External reference
The rise of deepfake medical imaging: radiologists’ diagnostic accuracy in detecting ChatGPT-generated radiographs
10.1148/radiol.252094 · External reference
The need to develop health data transaction disclosure requirements to balance transparency, privacy, and progressive use
10.1016/j.landig.2025.100947 · External reference
Evaluating the reasoning capabilities of large language models for medical coding and hospital readmission risk stratification: zero-shot prompting approach
10.2196/74142 · External reference
Improving model transferability for clinical note section classification models using continued pretraining
10.1093/jamia/ocad190 · External reference
