Machine Learning Depression Risk Assessment
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Solution Overview
Problem
Conventional methods for identifying depression in healthcare settings are subjective and inefficient, failing to accurately assess and intervene in potential depression risks due to the complexity of the condition and vast amount of data involved.
Innovation Solution
A machine learning-based method that processes user data to extract relevant attributes, generates risk scores, and initiates targeted interventions by training models on historical data, including structured and unstructured information, to provide objective and timely assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessments by healthcare providers are used to identify depression signs, then human expertise and judgment are applied, but the process is entirely subjective and frequently fails to identify those most in need of additional care
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated machine learning system that processes user data, extracts attributes, and generates depression risk scores objectively, eliminating human subjectivity while maintaining high identification accuracy
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw user data and depression risk assessment, using trained algorithms to process information and generate objective risk scores that bridge data and clinical decision-making
2Measurement precision
If healthcare providers manually evaluate all relevant data to identify potential depression concerns, then comprehensive assessment is attempted, but it is simply impossible for healthcare providers to evaluate all relevant data given the vast complexity involved in clinical depression
Solution Approach 1:
The patent segments the complex depression assessment task into distinct components: data extraction, attribute generation through machine learning models, and risk score calculation, allowing each component to be optimized independently and processed efficiently
Solution Approach 2:
The patent uses trained machine learning models that have been copied from training data to automatically replicate expert assessment patterns, enabling comprehensive data evaluation without requiring human providers to manually analyze every data point
3Loss of time
If conventional manual assessment methods are used, then healthcare providers can identify some depression risks, but the process is inefficient and fails to provide timely interventions
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on historical depression data, enabling the system to rapidly assess new users and generate risk scores immediately without requiring time-consuming manual evaluation
Solution Approach 2:
The patent implements self-service through automated machine learning models that independently process user data, extract relevant attributes, and generate depression risk assessments without requiring healthcare provider time and effort
Data Source
AI summary
Techniques for improved machine learning are provided. User data describing a user is received, and a set of user attributes corresponding to a defined set of features is extracted from the user data. A risk score is generated by processing the set of user attributes using a trained machine learning model, where the risk score indicates a probability that the user has or will develop depression. One or more interventions are initiated for the user based on the risk score.