Behavior Clustering for Early Depression Recurrence Prediction
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Solution Overview
Problem
Existing methods fail to accurately predict the recurrence and worsening of depression symptoms, leading to prolonged illness periods and decreased labor productivity, necessitating early intervention for effective treatment.
Innovation Solution
An information processing system and method that utilizes a clustering model to classify behavior patterns, generates feature amounts from activity and sleep data, and estimates psychological stress using an estimation model to anticipate depression symptom recurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction methods are used, then depression recurrence can be monitored, but prediction accuracy is insufficient leading to prolonged illness periods
Solution Approach 1:
The patent segments the prediction task into multiple components: behavior pattern clustering (grouping similar patients), feature amount generation (extracting activity and sleep metrics), and estimation model prediction (forecasting psychological stress). This segmentation allows each component to be optimized independently, improving overall prediction accuracy and enabling earlier intervention.
Solution Approach 2:
The system performs preliminary classification of behavior patterns and generation of feature amounts before the actual prediction of depression recurrence. By pre-processing data into clustered behavior patterns and extracted features, the system prepares prediction-ready information in advance, enabling more accurate and timely predictions of psychological stress changes.
2Measurement precision
If comprehensive behavior analysis is performed, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces intermediate components (clustering model and feature amount generation unit) that mediate between raw behavior data and the final prediction model. These intermediaries organize and structure the comprehensive behavior data into manageable clusters and features, maintaining prediction accuracy while reducing the complexity burden on the estimation model.
Solution Approach 2:
The system divides the complex analysis task into separate modules: behavior record processing, clustering analysis, feature extraction, and stress estimation. Each module handles a specific aspect of the analysis, allowing comprehensive behavior analysis to be performed systematically without overwhelming system complexity.
Data Source
AI summary
An object is to accurately predict recurrence and worsening of depression symptoms in advance. An information processing system (100) includes: a clustering unit (52) that inputs behavior record information of a user (U) to a clustering model that classifies behavior patterns into a plurality of clusters, and classifies behavior patterns of the user (U) into any of the plurality of clusters; a first feature amount generation unit (53) that generates a first feature amount indicating an activity state of the user U; and an estimation unit (55) that estimates a magnitude of psychological stress of the user (U) on the basis of object person information including attribute information of an object person, a cluster into which the behavior patterns of the object person are classified, and the first feature amount.