AI Dyadic Biomarker Prediction for Mental Distress
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Solution Overview
Problem
Conventional mental health assessment and management procedures are resource-intensive, time-intensive, and error-prone, failing to effectively process and integrate caregiver-related data, leading to inadequate management of mental distress and social rhythm disruptions in patients and caregivers.
Innovation Solution
The use of artificial intelligence techniques to analyze patient-caregiver dyadic biomarker data, predicting mental distress and social rhythm disruptions through phenotypic characterization and multivariate time series modeling with probabilistic transformers, and initiating automated interactions to mitigate these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mental health assessment procedures are used, then resource consumption and time consumption are high, but prediction accuracy and integration of caregiver data are insufficient
Solution Approach 1:
The patent merges patient data and caregiver data into a unified dyadic analysis framework. The system integrates multiple data sources including electronic health records, wearable device data, and caregiver reports into a single predictive model that assesses the patient-caregiver system as a whole, thereby improving prediction accuracy while avoiding the need for separate complex assessment procedures.
Solution Approach 2:
The patent introduces an artificial intelligence-based predictive analytics platform as an intermediary between raw data collection and clinical decision-making. This intermediary layer processes and integrates diverse data sources, transforming them into actionable predictions about mental distress and social rhythm disruption, thereby reducing the complexity burden on clinicians while maintaining high accuracy.
2Adaptability or versatility
If conventional assessment procedures are used, then data processing capability is limited, but implementation simplicity is maintained
Solution Approach 1:
The patent creates a universal predictive analytics platform that can process multiple types of data (electronic health records, wearable sensor data, caregiver reports) through a single integrated system. The AI model is designed to handle diverse data formats and sources uniformly, enabling versatile data processing without requiring separate specialized systems for each data type.
Solution Approach 2:
The patent replaces manual data processing and analysis mechanisms with automated artificial intelligence algorithms. The system automatically collects, integrates, and analyzes data from multiple sources using machine learning models, eliminating the need for manual data compilation and analysis while significantly enhancing data processing capability and adaptability.
3Measurement precision
If dyadic biomarker data analysis is implemented, then prediction accuracy improves, but computational resource requirements increase
Solution Approach 1:
The patent extracts and analyzes only the most relevant features and biomarkers from the dyadic data set using the predictive model. Rather than processing all available data equally, the system identifies and focuses on key predictive indicators, thereby maintaining high prediction accuracy while reducing the computational resources required for data processing.
Data Source
AI summary
Methods, systems, and computer program products for predicting states from patient-caregiver dyadic biomarker data using artificial intelligence are provided herein. A computer-implemented method includes obtaining biomarker data derived from one or more dyads, each dyad comprising at least one patient and at least one caregiver associated with the at least one patient; determining, based on processing the obtained biomarker data, data-based representations of mental distress and/or social rhythm disruption among at least one of the one or more dyads; predicting mental distress and/or social rhythm disruption among a given dyad of at least one patient and at least one caregiver associated with the at least one patient by processing input biomarker data, derived from the given dyad, using artificial intelligence techniques in connection with at least a portion of the data-based representations; and performing automated actions based on the predicting.