Genomic Testing Status Determination Using NLP
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Solution Overview
Problem
The challenge in cancer research is identifying eligible patients for clinical trials and matching them with appropriate trials based on their genomic testing status, which can change rapidly and is often inaccurately recorded.
Innovation Solution
A system and method using computer-generated algorithms and machine learning models to determine a patient's genomic testing status by analyzing unstructured patient record information, predicting the likelihood of genomic testing, and displaying a user interface with the genomic testing status and a link to the primary patient record.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If genomic testing status is manually recorded by physicians in handwritten notes, then the system is simple to operate, but the measurement precision and reliability of genomic testing status determination deteriorates
Solution Approach 1:
The patent replaces manual handwritten recording (mechanical system) with an automated natural language processing system that extracts genomic testing status from unstructured clinical notes. The NLP model automatically parses physician notes to identify genomic testing events, eliminating manual transcription errors while maintaining ease of data collection.
Solution Approach 2:
The system enables self-service by allowing the genomic testing status determination to occur automatically without requiring physician intervention for data entry. The NLP system independently processes clinical notes and updates patient records, reducing the operational burden on physicians while improving data accuracy.
2Reliability
If genomic testing status is updated in real-time from multiple patient records, then the reliability of trial eligibility determination improves, but the device complexity and processing time increases
Solution Approach 1:
The patent segments the complex task of genomic testing status determination into distinct processing stages: (1) collecting unstructured clinical notes from multiple sources, (2) applying NLP models to extract relevant information, (3) aggregating results across multiple patient records, and (4) determining final status. This segmentation reduces overall system complexity while maintaining reliability.
Solution Approach 2:
The system introduces an intermediary NLP processing layer between raw clinical notes and the genomic testing status database. This intermediary automatically parses and structures unstructured data, serving as a mediator that simplifies the integration of multiple data sources while ensuring accurate status determination.
3Measurement precision
If multiple machine learning models are used to determine genomic testing status, then the measurement precision improves, but the device complexity and computational resources increase
Solution Approach 1:
The patent divides the genomic testing status determination task into multiple specialized machine learning models, each trained to detect specific aspects of genomic testing information in clinical notes. This segmentation allows each model to focus on particular patterns, improving overall accuracy while maintaining manageable complexity through modular architecture.
Data Source
AI summary
A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.