Medical Text Learning Model for Diagnostic Code Automation
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Solution Overview
Problem
Current medical diagnostic processes are hindered by the time-consuming and inefficient processing of unstructured and semi-structured clinical data, leading to incomplete documentation of doctor-patient interactions, which can impact patient treatment.
Innovation Solution
A method and system that utilize Natural Language Processing (NLP) and machine learning techniques to automate the analysis of medical texts by tagging, validating, and modeling associations between medical texts and codes, generating a medical text learning model to assist in diagnosis, symptom identification, and treatment suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical professionals manually process free-text medical notes, then diagnostic accuracy can be maintained through human judgment, but time consumption increases significantly
Solution Approach 1:
The patent introduces an intermediary NLP-based coding system that acts as a mediator between unstructured medical notes and structured diagnostic data. The system automatically tags medical concepts with standardized codes (e.g., ICD-10, SNOMED-CT) while preserving semantic meaning, enabling both automated processing speed and maintained diagnostic accuracy through structured representation of clinical information.
Solution Approach 2:
The system transforms the parameter of data structure from unstructured free text to structured tagged text with standardized codes. This parameter change enables automated processing while maintaining the semantic richness needed for accurate diagnosis, effectively resolving the time-accuracy tradeoff by changing how information is represented rather than how it is processed.
2Loss of information
If comprehensive documentation of doctor-patient interactions is pursued, then complete diagnostic information is captured, but the complexity and time required for documentation increases
Solution Approach 1:
The system performs preliminary tagging of medical concepts during the documentation process itself, rather than requiring a separate coding step. As medical professionals write notes, the NLP system automatically identifies and tags medical entities with standardized codes in real-time, capturing complete diagnostic information while keeping the documentation interface simple and familiar.
Solution Approach 2:
The tagged medical text system serves multiple functions simultaneously: it preserves the natural language narrative for clinical context, provides structured codes for data analysis, enables automated information extraction, and maintains searchability. This multi-functionality captures comprehensive diagnostic information without increasing documentation complexity for the practitioner.
3Productivity
If automated tools are introduced to process medical texts, then time efficiency improves, but the accuracy and reliability of code assignment may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where tagged medical texts are continuously validated against standardized code sets and clinical guidelines. The NLP model learns from validated examples and adjusts its tagging accuracy, providing feedback loops that improve code assignment precision while maintaining automated processing speed. Medical professionals can also review and correct tags, with corrections feeding back into the training data.
Data Source
AI summary
Methods for assisting medical personnel in performing a diagnosis, diagnostic aids and computer readable media. The method initialisation step comprises: receiving a plurality of input medical texts tagged with potential medical codes; curating and validating the plurality of input medical texts to output a subset of medical texts that are validated and tagged with medical codes; and using the subset of tagged and validated medical texts to model the associations between the medical texts and the medical codes, and generating a medical text learning model based on the associations. The method diagnostic step comprises: inputting a specimen text relating to a patient into the medical text learning model; processing the specimen text using the medical text learning model; identifying suggested medical codes based on the specimen text; and outputting diagnoses, symptoms and treatments linked to the suggested medical codes for assisting medical personnel in providing a diagnosis for the patient.