A Robust BERT-Based Deep Learning Model for Automated Cancer Type Extraction from Unstructured Pathology Reports
The accurate extraction of clinical information from electronic medical records is particularly critical to clinical research but require much trained expertise and manual labor. In this study we developed a robust system for automated extraction of the specific cancer types for the purpose of supporting precision oncology research. from pathology reports using a fine-tuned RoBERTa model. This model significantly outperformed the baseline model and a Large Language Model, Mistral 7B, achieving F1_Bertscore 0.98 and overall exact match of 80.61%. This fine-tuning approach demonstrates the potential for scalability that can integrate seamlessly into the molecular tumour board process. Fine-tuning domain-specific models for precision tasks in oncology, may pave the way for more efficient and accurate clinical information extraction.
Code (0)
등록된 구현이 없습니다.
Tasks
Information ExtractionSimilar Papers 제목 키워드 기반
CancerBERT: a BERT model for Extracting Breast Cancer Phenotypes from Electronic Health Records
Accurate extraction of breast cancer patients' phenotypes is important for clinical decision support and clinical research. Current models do not take full advantage of cancer domain-specific corpus, whether pre-training…
NERExtracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model
Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observations of radiologists, creating an unstruct…
DiversityRelationRelation ExtractionCACER: Clinical Concept Annotations for Cancer Events and Relations
Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associ…
Event ExtractionIn-Context LearningModel OptimizationRelation ExtractionLeveraging 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…
Automated Detection of Clinical Entities in Lung and Breast Cancer Reports Using NLP Techniques
Research projects, including those focused on cancer, rely on the manual extraction of information from clinical reports. This process is time-consuming and prone to errors, limiting the efficiency of data-driven approac…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER