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Machine learning model for predicting the cold-heat pattern in Kampo medicine: a multicenter prospective

Ayako Maeda-Minami^1,2, Tetsuhiro Yoshino², Kotoe Katayama³

¹Faculty of Pharmaceutical Sciences, Tokyo University of Science, Chiba, Japan.

Frontiers in Pharmacology

|November 11, 2024

Summary

Machine learning accurately predicts cold-heat patterns in Kampo medicine patients. This predictive model, based on 148 symptoms and clinical data, achieved high accuracy, aiding in traditional medicine diagnosis.

Keywords:

Kampo medicine moderate (heat/cold) pattern prediction model tangled heat/cold pattern the International Classification of Diseases

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Area of Science:

Computational Medicine
Traditional East Asian Medicine
Machine Learning Applications

Background:

Cold-heat patterns are a key diagnostic concept in Kampo medicine.
Accurate identification of these patterns is crucial for effective treatment.
Existing diagnostic methods may benefit from computational approaches.

Purpose of the Study:

To develop and validate a machine learning model for predicting the four cold-heat patterns.
To apply artificial intelligence to subjective symptoms described in the International Classification of Diseases Traditional Medicine Conditions - Module 1.
To enhance the diagnostic accuracy of cold-heat patterns in Kampo outpatients.

Main Methods:

A multicenter prospective observational study involving 622 first-visit Kampo outpatients.
Development of separate cold and heat pattern prediction models using 148 symptoms, BMI, blood pressure, age, and sex.
Classification included single cold/heat, tangled heat/cold, and moderate (heat/cold) patterns.

Main Results:

The combined prediction models achieved high performance metrics: 96.7% accuracy, 93.2% macro-recall, 85.6% precision, and 88.5% F1-score.
Key predictive features aligned with established definitions of cold-heat patterns.
The model demonstrated robust predictive capabilities on the study cohort.

Conclusions:

A machine learning model was successfully developed to predict cold-heat patterns.
The model utilizes patient-reported symptoms that align with physician diagnoses.
This approach offers a promising tool for objective cold-heat pattern prediction in clinical practice.