Medical Report NLP Segmentation via Context-Free Grammar
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Solution Overview
Problem
Medical reports in heterogeneous formats and structures pose challenges for machine-operated systems to access and identify relevant information units for diagnosis decisions, as they often contain extensive, comprehensive texts with non-pathological findings that obscure pathological information.
Innovation Solution
A method using natural language processing and context-free grammar to segment, classify, and annotate medical reports, specifying language construction rules and integrating probabilistic information and expert knowledge to distinguish between pathological and non-pathological findings, enabling efficient information extraction and integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive medical reports are provided to ensure completeness of diagnosis, then diagnostic completeness is improved, but information retrieval difficulty increases due to extensive texts containing non-pathological findings
Solution Approach 1:
The patent segments comprehensive medical reports into distinct information units using natural language processing and context-free grammar. The system divides the extensive text into sentence-level units and further segments them into information units based on grammatical structures, allowing pathological findings to be separated from non-pathological findings. This segmentation enables machine-operated systems to efficiently locate relevant information without being overwhelmed by the complete report text.
Solution Approach 2:
The patent introduces an intermediary grammar-based classification system that acts as a mediator between the comprehensive medical report and the machine-operated system. The context-free grammar serves as an intermediary layer that structures the report content into hierarchical levels (sentence level, information unit level, word level) and assigns context classes, enabling efficient information retrieval while preserving the completeness of the original comprehensive report.
2Adaptability or versatility
If heterogeneous formats and structures of medical reports are processed directly, then data diversity is preserved, but machine access and processing efficiency deteriorate
Solution Approach 1:
The patent implements a universal grammar-based processing framework that can handle multiple heterogeneous medical report formats and structures. The context-free grammar is designed to be domain-specific yet format-agnostic, enabling the system to process various report types (radiology reports, pathology reports, etc.) through a unified approach. This universal framework maintains adaptability to different data formats while achieving consistent processing efficiency across diverse inputs.
Solution Approach 2:
The patent transforms heterogeneous report formats by changing their structural parameters into a standardized grammar-based representation. The system applies parameter changes at multiple levels: converting diverse sentence structures into standardized information units, transforming various formatting conventions into unified context class assignments, and normalizing different report types through consistent grammatical rules. This enables efficient machine processing while preserving the essential diversity of medical data.
3Reliability
If extensive comprehensive texts are used to verify completeness of diagnosis, then diagnostic thoroughness is improved, but identification of relevant information units becomes difficult
Solution Approach 1:
The patent applies local quality by assigning different context classes to different information units within the comprehensive report. Instead of treating the entire text uniformly, the system identifies and tags specific portions (pathological findings, non-pathological findings, diagnostic impressions) with appropriate context labels. This allows machine-operated systems to quickly identify and access relevant information units while preserving the completeness of the full diagnostic evaluation in the original comprehensive report.
Data Source
AI summary
A method for processing medical reports includes applying natural language processing methods to perform an initial segmentation of at least parts of textual contents of each medical report into information units, thereby identifying information units, such as text fragments, sentences or text passages, within the medical reports. The information units are classified into at least one context class to determine their appropriate context classes for a particular situation or application. The context classifications are created using a grammar, e.g., a context-free grammar, and can then be used for automatically assigning each information unit to an appropriate context meta-information. The medical report may be annotated by assigning the information units to context meta-information determined by the context class, and the context meta-information may be used by other applications, e.g., dedicated mechanisms for clinical data integration, how to efficiently handle the available information units within medical reports.