Machine Learning Depression Scoring and Relapse Detection
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Solution Overview
Problem
There is a need for improved methods and systems to monitor mental health and detect or predict depressive relapse due to the limited availability of healthcare providers, logistical challenges in continuous monitoring, and the stigma associated with seeking help, which can lead to untreated or poorly managed depression.
Innovation Solution
A system using machine learning models, such as neural networks, trained with general and user-specific data to generate depression scores and labels, integrating physiological, behavioral, and survey data to provide alerts and recommendations for users and healthcare providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of user behavior and mental state is implemented, then early detection of depressive relapse is improved, but cost and logistical complexity increase
Solution Approach 1:
The system segments monitoring into periodic assessments rather than truly continuous monitoring. Users complete surveys at scheduled intervals (e.g., daily or weekly), and the machine learning model processes these discrete data points to detect relapse patterns, achieving reliable early detection without the complexity of constant monitoring.
Solution Approach 2:
The system enables self-service monitoring where users independently complete surveys and provide data without requiring constant professional intervention. The machine learning algorithm automatically analyzes the data and generates alerts, reducing the need for complex human-in-the-loop monitoring infrastructure.
2Reliability
If periodic visits to healthcare providers are maintained, then mental health stability is monitored, but patient engagement decreases over time and early relapse signs are missed
Solution Approach 1:
The system implements automated feedback loops where the machine learning model continuously analyzes survey data and provides real-time feedback to both patients and providers. When relapse indicators are detected, the system automatically generates alerts and recommendations, maintaining monitoring consistency without requiring sustained high patient engagement.
Solution Approach 2:
The system performs preliminary detection of relapse signs through automated analysis of survey responses before patients would naturally seek help. The machine learning model identifies subtle patterns indicating emerging depression, enabling early intervention before patients disengage or fail to recognize their deteriorating state.
3Ease of operation
If more healthcare providers are deployed, then patient-to-provider ratio improves, but system cost increases
Solution Approach 1:
The system introduces an automated machine learning-based monitoring system as an intermediary between patients and healthcare providers. This intermediary continuously analyzes patient data and flags concerning patterns, allowing providers to focus their attention on patients who genuinely need intervention, thereby reducing the number of providers required while maintaining accessibility.
Solution Approach 2:
The system creates a virtual copy of the healthcare provider's monitoring capability through the machine learning algorithm. This digital assistant performs preliminary assessments and continuous monitoring that would otherwise require human providers, effectively multiplying the capacity of each provider without requiring additional human staff.
Data Source
AI summary
Systems and methods are provided herein for monitoring mental health and/or detecting and/or predicting a depressive relapse and/or determining a depressive state, label, and/or score using one or more machine learning models with one or more neural networks. Machine learning models may be trained using general data not specific to a certain user then may be tailored to a specific user and calibrated using user data that is associated with a time period as well as survey or other assessment data also associated with that time period. Newly generated user data may then be processed by the trained machine learning algorithm which may generate one or more score indicative a risk of a depression relapse and/or a depressive state and/or label. Based on the score, alerts, reports, and/or recommendations of actions for the user to take may be generated.