Deep Reinforcement Learning for Clinical Diagnosis Inference
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Solution Overview
Problem
Existing AI systems for clinical diagnosis inference struggle with free form clinical notes due to sparsity in annotated data and inability to capture linguistic complexities and ambiguities, limiting their ability to draw accurate conclusions.
Innovation Solution
A deep reinforcement learning approach is used to simulate the clinician's cognitive process by segmenting free form clinical notes into sentences, comparing them with external resources, and applying a reinforcement learning trained agent to infer differential diagnoses through a sentence-by-sentence analysis, optimizing a reward function for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If supervised classification models using neural networks are used for diagnosis inferencing, then the system can process clinical data, but the model heavily relies on large labeled data and lacks the ability to capture inherent ambiguities and complexities of clinical scenarios
Solution Approach 1:
The patent replaces traditional supervised classification models with a reinforcement learning agent that uses natural language generation to infer diagnoses. This substitution allows the system to handle free-form clinical notes and capture clinical ambiguities without relying on pre-labeled data, as the agent learns to generate diagnostic conclusions through iterative reinforcement learning rather than pattern matching on labeled examples.
Solution Approach 2:
The patent changes the fundamental parameters of the diagnosis system by transitioning from classification-based approaches to generation-based approaches. The reinforcement learning agent optimizes for diagnostic accuracy through reward functions rather than minimizing classification loss, and uses natural language generation to produce diagnoses rather than selecting from predefined labels, thereby capturing clinical nuances that fixed label sets miss.
2Device complexity
If medical codes and finite number of diagnoses are used for prediction labels, then the computational and linguistic difficulties are simplified, but the system loses the ability to capture linguistic complexities of clinical scenarios
Solution Approach 1:
The patent inverts the traditional approach by instead of simplifying clinical notes into fixed medical codes, using natural language generation to produce diagnostic conclusions directly from free-form text. The reinforcement learning agent generates diagnoses in natural language format, preserving the linguistic complexity and nuance of clinical scenarios while maintaining computational tractability through the reinforcement learning framework.
3Reliability
If graph-based reasoning methods are used to incorporate medical concepts and associations, then relevant medical knowledge can be integrated, but the approach does not focus on the intuitive and analytical processes of a clinician to infer probable diagnoses
Solution Approach 1:
The patent enables the reinforcement learning agent to independently perform the full diagnostic reasoning process that clinicians execute manually. The agent reads free-form clinical notes, generates differential diagnoses, evaluates them against medical knowledge, and produces final conclusions without requiring pre-processed graphs or structured inputs. This self-service approach mirrors the clinician's intuitive and analytical processes while incorporating medical knowledge through the reinforcement learning training.
Data Source
AI summary
Techniques are described herein for drawing conclusions using free form texts and external resources. In various embodiments, free form input data (202) may be segmented (504) into a plurality of input data segments. A first input data segment may be compared (510) with an external resource (304) to identify a first candidate conclusion. A reinforcement learning trained agent (310) may be applied (512) to make a first determination of whether to accept or reject the first candidate conclusion. Similar actions may be performed with a second input data segment to make a second determination of whether to accept or reject a second candidate conclusion. A final conclusion may be presented (522) based on the first and second determinations of the reinforcement learning trained agent with respect to at least the first candidate conclusion and the second candidate conclusion.