Digital Phenotyping in Telepsychiatry for Remote Diagnostic Accuracy
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Solution Overview
Problem
Telemedicine interactions face challenges in accurately diagnosing patients due to the lack of physical contact, missing vital signs, and reliance on verbal cues, complicating the detection of clinically relevant changes and patient assessment.
Innovation Solution
A system utilizing multimodal machine learning models to analyze audio, visual, and textual data, including wearable sensor data, to generate a personalized digital phenotype that provides quantitative data on an individual's biomarkers and symptom state trajectories, aiding practitioners in real-time diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telemedicine interactions are conducted without physical contact, then patient accessibility and convenience are improved, but diagnostic accuracy and assessment reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising sensors, machine learning models, and digital phenotype generation components that mediate between the telemedicine interaction and diagnostic assessment. This intermediary captures and analyzes multiple data modalities (audio, visual, textual, wearable sensor data) to compensate for the lack of physical contact, thereby maintaining diagnostic accuracy while preserving patient accessibility
Solution Approach 2:
The system performs multiple functions within a single telemedicine platform: it conducts the clinical interview, captures various data modalities, processes information through machine learning models, generates digital phenotypes, and provides diagnostic support. This multi-functionality allows the system to maintain comprehensive assessment capabilities while operating in a remote setting
2Device complexity
If traditional clinical assessment methods are used in telemedicine, then implementation simplicity is maintained, but measurement precision and detection capability deteriorate
Solution Approach 1:
The assessment system is segmented into distinct functional components: data capture from multiple modalities (audio, visual, textual, wearable sensors), machine learning model processing, digital phenotype generation, and clinical decision support. This segmentation allows each component to be optimized independently while maintaining overall system functionality and measurement precision
3Ease of manufacture
If finite interactions in artificial clinical settings are used, then setup simplicity is maintained, but information completeness and assessment quality deteriorate
Solution Approach 1:
The system merges multiple data sources and modalities that would traditionally require separate assessment tools and settings: audio recordings, visual video feeds, textual transcriptions, and wearable sensor data are combined and integrated through machine learning models to create a comprehensive digital phenotype, thereby capturing complete patient information within a single telemedicine session
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for improving for improving telemedicine (e.g., remote) interactions by capturing multiple types of data (e.g., audio, visual, textual), using a series of machine learning models to generate predictions from the data, and providing the predictions to a provider during the telemedicine interaction. One or more machine learning models may be utilized to generate intermediate representations of features extracted from audio, visual, and textual data. The data may be of a target individual involved in a remote interaction such as a telemedicine interaction, a job coaching session, or other scenario. The intermediate representations may be input to a machine learning model configured to generate a digital phenotype of the target individual. The digital phenotype may indicate a predicted diagnosis of the target individual, may indicate sub-clinical biomarkers of the target individual, as well as a projected trajectory of the predicted diagnosis.