Search research articles

Related Concept Videos

Introduction to Language of Pathophysiology l

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like...

Introduction to Language of Pathophysiology ll

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.

Scientific reports·2026

Same author

Intelligent underwater mine detection using multi-backbone feature fusion with temporal attention-gated unit on side-scan sonar imaging.

Scientific reports·2026

Same author

Artificial intelligence with deep convolutional neural network-based clinical decision-making on kidney Oncology using multimodal imaging.

Scientific reports·2026

Same author

Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement learning with frilled lizard algorithm.

Scientific reports·2025

Same author

An efficient trustworthy cyberattack defence mechanism system for self guided federated learning framework using attention induced deep convolution neural networks.

Scientific reports·2025

Same author

An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data.

Scientific reports·2025

Same journal

Determinants of Acute Kidney Injury After Endoscopic Retrograde Cholangiopancreatography in Patients With Liver Cirrhosis: Retrospective Observational Study.

JMIR medical informatics·2026

Same journal

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026

Same journal

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

JMIR medical informatics·2026

Same journal

Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.

JMIR medical informatics·2026

Same journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026

Same journal

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 2, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Medical Feature Extraction From Clinical Examination Notes: Development and Evaluation of a Two-Phase Large Language

Manal Abumelha^1,2, Abdullah Al-Malaise Al-Ghamdi^1,3, Ayman Fayoumi¹

¹Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

JMIR Medical Informatics

|October 31, 2025

Summary

This summary is machine-generated.

This study introduces a novel two-phase framework for medical feature extraction using large language models (LLMs). The framework significantly enhances accuracy and reduces hallucinations and missing features, even with limited training data.

Keywords:

automated medical assessment clinical NLP hallucination mitigation instruction tuning large language models medical feature extraction semantic matching

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: May 2, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Natural Language Processing
Medical Informatics
Machine Learning

Background:

Medical feature extraction from clinical text is hindered by data scarcity and terminology variations.
Large language models (LLMs) show promise but struggle with hallucination in medical applications.

Purpose of the Study:

To develop a robust LLM framework for accurate medical feature extraction.
To minimize hallucination and improve performance with limited training data.

Main Methods:

A two-phase training approach was implemented: instructing fine-tuning and confidence-regularization fine-tuning.
The model was trained on full (700 notes) and few-shot (100 notes) datasets.
Evaluation utilized the USMLE Step-2 Clinical Skills dataset with extensive testing.

Main Results:

Achieved high F1-scores (0.968-0.983 on full, 0.960-0.973 on few-shot data), outperforming existing methods.
Reduced hallucinations by 89.9% and missing features by 88.9% compared to baseline LLM.
Demonstrated stable model confidence despite performance improvements.

Conclusions:

The two-phase LLM framework offers state-of-the-art medical feature extraction with reduced errors.
The framework exhibits strong generalization, performing well with minimal data for resource-constrained settings.
The approach provides reliable outputs for automated clinical assessment.