Extract oncological entities

Leveraging a sophisticated data extraction model that identifies over 50 oncology-specific entities such as therapies, tests, and oncogenes from clinical documentation enhances the precision of patient care strategies. This technology not only streamlines oncology workflows but also advances personalized cancer treatment by systematically organizing and analyzing vital data. Use the provided Streamlit playground application to test this service. <p><br/></p> **Entity recognition**: Initially, the model accurately identifies entities such as Adenopathy, Age, Biomarker, Biomarker_Result, Cancer_Dx, Cancer_Score, and much more <p><br/></p> **Assertion Status** Detection: Subsequently, it assigns an assertion status to each identified entity (e.g.Present, Absent, Possible, Past, Family, Hypotetical) <p><br/></p> **Relation Extraction Labels**: The final step involves the detection of relationships between the extracted entities**** (e.g. is_size_of, is_finding_of, is_date_of, Date-Cancer_Dx, Tumor_Finding-Site_Breast, Tumor_Finding-Site_Bone, Tumor_Finding-Site_Liver, Tumor_Finding-Site_Lung, and much more)

搜集汇总

背景与挑战

背景概述

该数据集利用先进模型从临床文档中提取50多种肿瘤学相关实体（如疗法、检测、癌基因等），并通过实体识别、断言状态分配和关系提取三个步骤，优化肿瘤工作流程并推动个性化癌症治疗。