Bayesian Rule List Model for Interpretable Mental Health Diagnosis
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Solution Overview
Problem
Current machine learning models in clinical psychiatry, such as artificial neural networks, are often black box in nature, making it difficult for clinicians to interpret and validate their decisions, which is crucial for regulatory scrutiny and clinical soundness, and they struggle to provide interpretable results using multiple modalities and large datasets prevalent in psychiatric disorders.
Innovation Solution
Development of interpretable machine learning methods, specifically using Bayesian Rule Lists (BRL) and Multiple Correspondence Analysis (MCA) based rule mining, to generate predictable and interpretable models that can process multiple modalities like clinical scales, psychological assessments, and MRI data, producing concise rule lists that clinicians can understand and validate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial neural networks and ensemble models are used for psychiatric data analysis, then predictive accuracy is improved, but interpretability deteriorates making the models black box
Solution Approach 1:
The patent introduces Bayesian Rule Lists as an intermediary model that translates complex neural network predictions into interpretable if-then rules. This mediator layer maintains the predictive power of deep learning while providing clinically understandable explanations through sequential rule evaluation.
Solution Approach 2:
The patent replaces the mechanical black-box neural network system with a Bayesian probabilistic system that generates interpretable rule lists. This substitution maintains accuracy while replacing the opaque decision-making process with transparent, explainable logic that clinicians can validate.
2Measurement precision
If multiple modalities and large datasets are processed, then screening accuracy is improved, but computational complexity and data processing difficulty increase
Solution Approach 1:
The patent segments multiple data modalities (clinical scales, psychological assessments, MRI data) into separate processing streams that are independently analyzed by specialized models. Each modality is processed through appropriate preprocessing and feature extraction before being integrated into the final Bayesian Rule List, reducing overall system complexity.
Solution Approach 2:
The patent creates a universal Bayesian Rule List framework that can process multiple data types and modalities through a single unified model structure. This multi-functional approach allows the same interpretability mechanism to handle diverse inputs including structured clinical data, unstructured text, and imaging data.
3Reliability
If Bayesian Rule Lists are used for interpretability, then clinical validation is improved, but model flexibility and adaptability may be reduced
Solution Approach 1:
The patent implements dynamic Bayesian Rule Lists that can adapt to different clinical contexts and patient populations. The rule weights and thresholds are not fixed but can be adjusted through Bayesian updating as new data becomes available, maintaining clinical interpretability while enabling continuous adaptation to emerging patterns.
Solution Approach 2:
The patent allows flexible parameter adjustment in the Bayesian Rule Lists, including prior distributions, likelihood functions, and decision thresholds. These parameters can be tuned to balance interpretability and flexibility, and can be modified without changing the fundamental rule-based structure, enabling adaptation to different clinical scenarios.
Data Source
AI summary
A system includes a display device, a user interface, a memory, and a control system. The memory contains machine readable medium including machine executable code storing instructions for performing a method. The control system is coupled to the memory, and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to display, on the display device, a series of questions from mental health questionnaires. The series of questions includes text and answers for each question. From the user interface, a selection of answers of each of the displayed series of questions is received from a patient. Using a Bayesian Decision List, the received selection of answers is processed to output an indication of mental health of the patient. The indication of mental health identifies a kappa opioid receptor antagonist to which the patient would likely be a higher responder.