Certainty Qualification in Diagnostic Reports via Deep Learning
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Solution Overview
Problem
Diagnostic ambiguity in radiology reports leads to overutilization of follow-up imaging studies, delayed patient care, and inappropriate treatment due to the lack of precision in conveying diagnostic confidence, which existing methods using standardized lexicons and natural language processing fail to adequately address, especially in context-dependent scenarios.
Innovation Solution
A deep-learning based method that utilizes a pre-trained bidirectional encoder representations from transformers (BERT) model fine-tuned for certainty assessment in diagnostic reports, allowing for contextualized certainty classification and interactive feedback to improve report precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standardized lexicon is used to assess diagnostic certainty, then lexical-level interpretation is improved, but context-dependent diagnostic confidence is lost
Solution Approach 1:
The patent introduces NLP technology as an intermediary between the standardized lexicon and the diagnostic report. This intermediary processes the free text to extract contextual information and maps it to certainty levels, thereby preserving context-dependent diagnostic confidence while still utilizing the structured approach of standardized lexicons.
Solution Approach 2:
The system changes the parameter of certainty assessment from purely lexical matching to a contextualized interpretation. By using NLP to analyze the semantic meaning and context of diagnostic statements, the system transforms how certainty is measured, moving beyond word-by-word lexicon matching to understand the overall diagnostic confidence expressed in the report.
2Difficulty of detecting and measuring
If conventional NLP methods are used for uncertainty analysis, then detection of hedging terms is improved, but contextual understanding and quantitative assessment are limited
Solution Approach 1:
The patent replaces conventional mechanical NLP methods (keyword matching, rule-based detection) with a deep learning-based natural language understanding system. This substitution enables the system to comprehend the semantic context of diagnostic statements and provide quantitative certainty assessments rather than merely detecting hedging terms.
Solution Approach 2:
The system combines multiple NLP techniques and deep learning models to create a composite assessment mechanism. By integrating various NLP components that analyze different aspects of the diagnostic report (hedging terms, differential diagnoses, statement structure), the system achieves both detection capability and contextual understanding with quantitative output.
3Adaptability or versatility
If diagnostic reports use free text expressions, then communication flexibility is improved, but interpretation variability between physicians increases
Solution Approach 1:
The patent implements a feedback mechanism where the NLP-based certainty assessment system analyzes free text diagnostic reports and provides quantitative certainty levels back to the reporting process. This feedback loop helps standardize interpretation by providing objective measurements of diagnostic confidence, reducing variability between different physicians' interpretations of the same free text expressions.
Data Source
AI summary
A method for assessing diagnostic certainty in diagnostic reporting natural language, the method comprising receiving a natural language impression portion of a diagnostic report submitted for certainty evaluation, the impression portion having one or more sentences of natural language, accessing a pre-trained and fine-tuned language model, applying the one or more sentences to the trained language model for evaluation of the one or more sentences as a whole, receiving an assessment of certainty for the respective one or more sentences, based on the evaluation, communicating the assessment of certainty to a user before accepting the impression portion, and accepting submission of the impression portion only after the impression portion satisfies certainty criteria, or if the certainty criteria is not required obtaining validation from the user.