Questionnaire-Based Mental Illness Diagnosis With Network Drug Matching
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Solution Overview
Problem
Current mental health care systems face challenges in cost-effectiveness and accessibility due to the lack of objective diagnostic criteria and subjective clinical judgment in mental illness diagnosis, with existing AI-based clinical decision support systems focusing primarily on medication choice rather than diagnosis.
Innovation Solution
A method and system utilizing a network information providing unit, clustering unit, and drug prescription information deriving unit to classify mental illness groups based on questionnaire data, providing objective diagnosis and prescription information through k-means and Louvain algorithms, and deriving network-based and cluster-based drug prescription information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective clinical judgment is used for mental illness diagnosis, then diagnostic flexibility is maintained, but diagnostic objectivity and consistency deteriorate
Solution Approach 1:
The patent replaces the mechanical system of subjective clinical judgment with an AI-based computational system that processes questionnaire data through network construction and clustering algorithms. This substitution transforms mental illness diagnosis from a subjective art into an objective data-driven process, achieving diagnostic consistency while maintaining system manageability through automated processing.
2Adaptability or versatility
If AI-based CDSS focuses only on medication choice, then drug selection is improved, but comprehensive diagnosis capability deteriorates
Solution Approach 1:
The patent creates a multi-functional AI system that simultaneously performs both diagnosis and medication recommendation. The system constructs symptom networks, classifies mental illness types, identifies symptom clusters, and recommends treatments within a single integrated framework. This universal approach prevents information loss by ensuring diagnostic findings directly inform treatment decisions, making the CDSS adaptable to comprehensive mental health care needs.
3Productivity
If traditional mental health care systems are used, then accessibility is maintained, but cost-effectiveness deteriorates
Solution Approach 1:
The patent enables a form of self-service in mental health care through automated AI processing of questionnaire data. The system independently performs diagnostic evaluation, symptom classification, and treatment recommendation without requiring extensive clinician time for each assessment. This self-service capability dramatically improves care productivity and cost-effectiveness, making comprehensive mental health evaluation accessible at scale.
Data Source
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AI summary
The present invention relates to a method for mental illness diagnosis and drug recommendation using a mental health questionnaire. The method may provide highly reliable and accurate mental illness diagnosis information and information on medication based on a large amount of data, thereby improving medical quality and reducing medical costs.