Transparent Diagnostic System for Mental Health
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Solution Overview
Problem
Existing machine learning algorithms for medical diagnosis are often black-box systems that lack transparency and explainability, leading to potential misdiagnosis and limited adoption in clinical settings.
Innovation Solution
The system employs machine learning models such as BiGRU, BiLSTM-H, and LLM, combined with a rule-based parser, to identify diagnostic criteria in natural language sentences and provide transparent, explainable diagnoses consistent with established medical guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If black-box machine learning algorithms are used for medical diagnosis, then diagnostic speed and automation are improved, but transparency and explainability deteriorate
Solution Approach 1:
The patent introduces an intermediary component that extracts and explains the reasoning behind machine learning diagnostic decisions. This intermediary layer translates the black-box algorithm's output into interpretable clinical criteria, allowing practitioners to understand the diagnostic rationale without sacrificing automation speed.
Solution Approach 2:
The diagnostic system is segmented into distinct components: the machine learning model for rapid diagnosis and the explanation generator for transparency. This segmentation allows each component to specialize - the ML model focuses on accuracy and speed while the explanation component focuses on interpretability, resolving the contradiction between productivity and information loss.
2Extent of automation
If black-box machine learning algorithms are used for medical diagnosis, then automation extent is improved, but reliability deteriorates due to potential misdiagnosis
Solution Approach 1:
The system implements feedback by providing practitioners with explainable diagnostic criteria and rationale from the machine learning model. This feedback loop allows clinicians to verify, challenge, or supplement the automated diagnosis, thereby maintaining high automation while ensuring reliability through human-in-the-loop validation.
Solution Approach 2:
The machine learning model performs preliminary diagnostic assessment automatically, but the system prepares for potential misdiagnosis by providing transparent reasoning that enables practitioners to conduct preliminary review before finalizing the diagnosis. This preliminary action approach balances automation with reliability.
3Productivity
If black-box machine learning algorithms are used for medical diagnosis, then productivity is improved, but loss of information deteriorates due to lack of rationale
Solution Approach 1:
An intermediary explanation component bridges the gap between the machine learning model's efficient but opaque decision-making and the clinical need for transparent rationale. This intermediary extracts and presents the diagnostic criteria and reasoning in a clinically meaningful format, preserving information without reducing productivity.
Solution Approach 2:
The system adds another dimension to the diagnostic output by not only providing the diagnostic label (one dimension) but also the explanatory rationale and criteria (additional dimension). This dimensional expansion preserves complete information while maintaining the efficiency of automated diagnosis.
Data Source
AI summary
A system that uses a rule-based parser and/or one or more machine learning models—e.g., a bidirectional gated recurrent unit (BiGRU) model, a hybrid bidirectional long short-term memory (BiLSTM-H) model, and/or a multilabel BiLSTM (BiLSTM-M) model, a large language model (LLM)—to label natural language sentences as indicative of diagnostic criteria used to diagnose and/or assess the severity of a mental disorder or other medical condition. In some embodiments, the system also determines whether the identified diagnostic criteria are indicative of a disorder under established medical guidelines and provides a final diagnostic label. By outputting both a diagnosis consistent with established medical guidelines and an understanding of the identified diagnostic criteria used to make that diagnosis, the disclosed system provides more clinical value to practitioners than the existing “black box” models used for medical diagnoses.