Multimodal Analysis System for Mental Health Screening
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Solution Overview
Problem
Current mental health screening methods are inadequate in addressing the growing mental health epidemic, with limited access to mental health professionals and ineffective treatments leading to a significant burden on healthcare systems.
Innovation Solution
A multimodal analysis system utilizing artificial intelligence and machine learning to analyze video, audio, and speech content separately and in combination, extracting patterns specific to mental disorders and assigning likelihood scores, with the option to integrate additional modalities for enhanced sensitivity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated multimodal analysis is implemented, then mental health screening accuracy and accessibility are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the complex analysis task into separate modular components: video analysis module, audio analysis module, speech content analysis module, and fusion module. Each module processes specific data streams independently before results are combined, making the overall complex system more manageable and maintainable while achieving high screening accuracy through specialized processing of each modality
Solution Approach 2:
The patent merges multiple data streams (video, audio, speech content) and their extracted features into a unified analysis framework. By combining information from different modalities through feature extraction and fusion, the system achieves comprehensive mental health assessment that exceeds the capability of single-modality approaches, thereby improving screening accuracy
2Reliability
If multiple modalities are integrated for analysis, then screening sensitivity and comprehensive assessment are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction from each modality (video, audio, speech) before the final fusion and classification steps. By pre-processing and extracting relevant features in advance, the system reduces the computational burden during the critical fusion phase, thereby decreasing overall processing time while maintaining high sensitivity through comprehensive multi-modality integration
3Ease of operation
If late fusion scheme is used, then model interpretability is improved, but computational complexity of feature extraction increases
Solution Approach 1:
The late fusion architecture segments the processing pipeline into distinct stages: independent feature extraction for each modality, separate analysis of each data stream, and final fusion of results. This segmentation allows the system to maintain high interpretability by analyzing each modality separately while managing computational complexity through modular processing of features from different sources
Data Source
AI summary
Embodiments may provide improved techniques for mental health screening and its provision. For example, a method may comprise receiving input data relating to communications among persons, the input data comprising a plurality of modalities, extracting features relating to the plurality of modalities from the received input data, performing multimodal fusion on the extracted features, wherein the multimodal fusion is performed on at least some of the features relating to individual modalities and on at least some combinations of features relating to a plurality of modalities, classifying the fused features using a trained model for detection of at least one mental disorder, and generating a representation of a disorder state based on the classified fused features. For the multimodal fusion, a late fusion scheme instead of early fusion may be used to make the model more interpretable and explainable without compromising the performance.