Automated Patient Disease Context Identification Using Evidentiary Timelines
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Solution Overview
Problem
In clinical workflows, clinicians face challenges in gathering and analyzing patient records due to the complexity of radiology and pathology exams, often leading to incomplete context and increased risk of misdiagnosis, as existing methods do not effectively consider the semantic content or relationships between exams.
Innovation Solution
A system and method for classifying and clustering medical reports, including radiology and pathology reports, by processing documents, extracting entities, determining anatomy inference, and using machine learning to estimate similarities and visualize relevant reports, thereby linking and presenting relevant patient data in different disease contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually gather and analyze patient records, then they can review detailed information, but the process is time-consuming and prone to incomplete context
Solution Approach 1:
The system automatically performs the gathering and analysis of patient records without requiring manual clinician intervention. The automated workflow extracts entities, determines anatomy inferences, estimates similarities, and clusters reports independently, allowing clinicians to review pre-organized results rather than manually processing raw data.
Solution Approach 2:
The manual mechanical process of reviewing records is replaced with an automated computational system using NLP, machine learning models, and clustering algorithms. The system processes documents, extracts entities, and organizes reports automatically, substituting human manual analysis with computational automation.
2Ease of operation
If existing methods are used to review prior exams, then the process is simple, but semantic content and relationships between exams are not effectively considered
Solution Approach 1:
The system introduces an intermediary processing layer between the raw medical reports and the clinician review. This intermediary automatically extracts entities, determines anatomy inferences, estimates semantic similarities using machine learning, and clusters reports by disease context, thereby preserving and organizing semantic information that would otherwise be lost in simple chronological review.
Solution Approach 2:
The system performs preliminary analysis of patient records before clinician review. By pre-extracting entities, pre-determining anatomy inferences, and pre-clustering reports by disease context, the system prepares the information in advance, making the actual clinician review simpler while ensuring semantic relationships are captured.
3Reliability
If all prior exams are reviewed to ensure complete context, then diagnostic accuracy improves, but the complexity of analysis increases significantly
Solution Approach 1:
The system segments the large set of prior exams into smaller, meaningful clusters based on disease context and semantic similarity. Instead of presenting all exams uniformly, the system divides them into grouped categories (e.g., by anatomy, pathology type, or disease progression), making the complex set manageable while ensuring comprehensive coverage of relevant context.
Solution Approach 2:
The system changes the organizational parameter from simple chronological ordering to semantic similarity-based clustering. By transforming the data structure from time-based sequences to meaning-based groups, the system reduces analytical complexity while improving diagnostic reliability through better contextual organization.
Data Source
AI summary
A method for classifying medical reports of a patient, including: receiving a plurality of patient medical reports; processing the plurality of patient medical reports to produce a processed report that extracts patient medical information; estimating the similarity between the plurality of medical reports based upon the extracted patient medical information; clustering similar medical reports; inferring a group type for the clustered medical reports and labeling the clustered medical reports with the inferred group type; and visualizing the labelled clustered medical reports on a display.