LLM Clinical Criteria Matching With Response Variability Confidence
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Solution Overview
Problem
Existing large language models (LLMs) lack a mechanism to determine the accuracy of their inference responses, hindering their use in clinical reasoning tasks due to the need for high confidence scores mandated by medical regulatory bodies.
Innovation Solution
A two-stage approach is employed to generate a confidence score for LLM responses, involving input variation and response assessment, utilizing an LLM to address the lack of accuracy of the LLM responses, the disclosed techniques provide a mechanism to generate a confidence score for L the LLM to generate a confidence score for the LLM to generate a confidence score for the LLM responses, involving input variation and response assessment, utilizing an LLM to analyze EHR information and clinical criteria, and a response assessment component to determine a final response and confidence score based on variability in responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM is used to generate inference responses to clinical questions, then the capability to answer clinical questions is improved, but the reliability of responses deteriorates due to lack of confidence score mechanism
Solution Approach 1:
The system implements a feedback mechanism by generating multiple inference responses through varied input formulations and then assessing these responses against the clinical question and patient data. The response assessment component analyzes the consistency and quality of multiple responses to generate a confidence score, creating a self-evaluation feedback loop that improves reliability without sacrificing the LLM's clinical question-answering capability
Solution Approach 2:
The system performs preliminary actions by generating multiple variations of input data and multiple inference responses before final assessment. The input variation component creates several versions of the clinical question input, and the inferencing component generates multiple responses for each variation, allowing the system to evaluate response consistency and reliability before committing to a final answer
2Measurement precision
If multiple variations of input data are generated and processed, then the confidence score accuracy is improved, but the computational time and complexity increase
Solution Approach 1:
The system applies partial action by generating a limited but sufficient number of input variations and inference responses rather than exhaustively processing all possible variations. The response assessment component evaluates a representative sample of responses to generate the confidence score, achieving adequate precision without excessive computational overhead
Solution Approach 2:
The system segments the confidence score generation process into distinct components: input variation generation, inferencing for each variation, and response assessment. This segmentation allows parallel processing of multiple input variations and their corresponding inference responses, reducing overall computational time while maintaining accuracy through systematic evaluation of each segment
Data Source
AI summary
Techniques for large language model (LLM) based patient to clinical treatment criteria matching with confidence score generation are described. In an example, a computer-implemented method can comprise generating different variations of textual input data for a LLM configured to generate an inference response to a clinical question regarding a patient and having a categorical answer, wherein the textual input data comprises a textual prompt of the clinical question, patient data comprising electronic medical record information for the patient and clinical criteria data comprising clinical criteria related to the clinical question. The method further comprising applying the LLM to the different variations and generating inference responses for each of the different variations, determining a final inference response to the clinical question based on a combination of the inference responses, and generating a confidence score for the final inference response based on a measure of variability between the inference responses.