Digital phenotyping method, device and computer program for drug response classification and prediction

The digital phenotyping method accurately classifies dementia types using electroencephalogram data and multiple diagnostic models, addressing the limitations of conventional diagnosis methods by enhancing treatment precision and clinical subject selection.

JP2025536904APending Publication Date: 2025-11-12IMEDISYNC INC
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Patent Information

Application Number
JP2025521065
Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
Priority Date
2022-10-25
Filing Date
2022-11-03
Publication Date
2025-11-12

AI Technical Summary

Technical Problem

Conventional methods for diagnosing dementia patients are unable to accurately classify patients into specific types, such as Alzheimer's disease and Lewy body dementia, leading to inappropriate treatment prescriptions and limitations in pharmaceutical development.

Method used

A digital phenotyping method and device that analyzes biological data, particularly electroencephalogram data, using multiple diagnostic models to accurately classify dementia types, including Alzheimer's disease, Lewy body dementia, Parkinson's disease, vascular dementia, depression, and anxiety, by training diagnostic models on classified data and calculating probability values for each disease.

Benefits of technology

Enables precise classification of dementia types, allowing targeted treatment prescriptions and selecting appropriate clinical subjects, thereby improving treatment efficacy and pharmaceutical development accuracy.

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Abstract

A digital phenotyping method, device, and computer program for drug response classification and prediction according to various embodiments of the present invention is a method performed by a computing device, comprising: acquiring biological data of a patient; and performing a multi-disease diagnosis for the patient by analyzing the acquired biological data through a disease diagnostic model, the disease diagnostic model including a plurality of diagnostic models that independently diagnose each of a plurality of different diseases based on the acquired biological data.
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Citation Information

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