Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection
We use a broad coverage, linguistically precise English Resource Grammar (ERG) to detect negation scope in sentences taken from pathology reports. We show that incorporating this information in feature extraction has a positive effect on classification of the reports with respect to cancer laterality compared with NegEx, a commonly used tool for negation detection. We analyze the differences between NegEx and ERG results on our dataset and how these differences indicate some directions for future work.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationNegationNegation DetectionSimilar Papers 제목 키워드 기반
Trust but Verify:Evidence-Linked Multi-Agent Clinical Information Extraction in Pathology
Clinical feature extraction from pathology reports is challenging because relevant evidence may be distributed across coded and narrative fields and depend on specimen attribution, negation, ancillary findings, and diagn…
Leveraging large language models for structured information extraction from pathology reports
Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-consuming and expensive, limiting scalabil…
Pathology Extraction from Chest X-Ray Radiology Reports: A Performance Study
Extraction of relevant pathological terms from radiology reports is important for correct image label generation and disease population studies. In this letter, we compare the performance of some known application progra…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Natural Language Understanding+1Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports
Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large language models (LMs) and retrieval augmented…
DiagnosticModel SelectionPrivacy PreservingPrompt Engineering+4Automated Histopathology Report Generation via Pyramidal Feature Extraction and the UNI Foundation Model
Generating diagnostic text from histopathology whole slide images (WSIs) is challenging due to the gigapixel scale of the input and the requirement for precise, domain specific language. We propose a hierarchical vision …