Search research 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

Related Concept Videos

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

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

Sort by

Same author

Evaluation of large language models in medical examinations: A scoping review protocol.

PloS one·2026

Same author

TCMEval-PA: a question-answering benchmark dataset for the prescription audit of Traditional Chinese Medicine.

Scientific data·2025

Same author

TCMEval-SDT: a benchmark dataset for syndrome differentiation thought of traditional Chinese medicine.

Scientific data·2025

Same author

Visualization of Traditional Chinese Medicine Formulas: Development and Usability Study.

JMIR formative research·2023

Same author

TCDO: A Community-Based Ontology for Integrative Representation and Analysis of Traditional Chinese Drugs and Their Properties.

Evidence-based complementary and alternative medicine : eCAM·2021

Same author

AMFormulaS: an intelligent retrieval system for traditional Chinese medicine formulas.

BMC medical informatics and decision making·2021

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

Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026

Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026

Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026

Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

JMIR medical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 14, 2025

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

A Weighted Voting Approach for Traditional Chinese Medicine Formula Classification Using Large Language Models:

Zhe Wang^1,2, Keqian Li³, Suyuan Peng⁴

¹Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences; School of Basic Medicine, Peking Union Medical College, Beijing, China.

JMIR Medical Informatics

|July 24, 2025

Summary

This summary is machine-generated.

This study shows that ensemble learning with large language models (LLMs) significantly improves traditional Chinese medicine (TCM) formula classification accuracy. The best ensemble model achieved 77.15% accuracy, enhancing TCM knowledge discovery.

Keywords:

TCM formula classification algorithm development ensemble learning large language models traditional Chinese medicine

More Related Videos

Preparation of Gynura bicolor DC samples for High-Resolution Tandem Mass Spectrometry

Preparation of Gynura bicolor DC samples for High-Resolution Tandem Mass Spectrometry

Published on: February 2, 2024

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: Sep 14, 2025

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

Preparation of Gynura bicolor DC samples for High-Resolution Tandem Mass Spectrometry

Preparation of Gynura bicolor DC samples for High-Resolution Tandem Mass Spectrometry

Published on: February 2, 2024

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:

Biomedical Informatics
Computational Linguistics
Traditional Chinese Medicine

Background:

Traditional Chinese Medicine (TCM) formulas possess critical information on ingredients, efficacy, and indications.
Classifying TCM formulas is essential for standardization, clinical support, research, and modernization.
Large Language Models (LLMs) offer advanced capabilities for enhancing TCM knowledge discovery.

Purpose of the Study:

To evaluate the performance of various LLMs in classifying TCM formulas.
To improve TCM formula classification accuracy using ensemble learning with fine-tuned LLMs.

Main Methods:

Manually curated and cleaned dataset of 2441 TCM formulas.
Fine-tuned 10 Chinese-supporting LLMs.
Employed ensemble learning with hard and weighted voting mechanisms.
Selected top-performing models for ensemble voting (top 5 and top 3).

Main Results:

Qwen-14B achieved the highest single-model accuracy at 75.32%.
Ensemble methods improved accuracy: hard voting (75.79%), weighted voting (76.47%), weighted voting (top 5) (75.57%), and weighted voting (top 3) (77.15%).

Conclusions:

Ensemble learning with LLMs effectively enhances TCM formula classification accuracy.
The proposed method improves the classification system for TCM formula efficacy.
This approach accelerates TCM knowledge discovery and promotes scientific application.