Knowledge Extraction of Long-Term Complications from Clinical Narratives of Blood Cancer Patients with HCT Treatments

Weizhong Zhu, J. B. Teh, Haiqing Li, S. Armenian
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引用次数: 1

Abstract

Interactive information extraction (IE) systems supported by biomedical ontologies are intelligent natural language processing (NLP) tools to understand literature and clinical narratives and discover meaningful domain knowledge from unstructured text. This study developed integrated IE systems to detect treatment complications of blood cancer patients from Electrical Medical Records (EMR) in the Long-Term Follow-Up (LTFU) protocol following Hematopoietic Cell Transplantation (HCT). The performance of the proposed approach was very encouraging compared to the gold-standard datasets manually reviewed by domain experts. In addition, the NLP system identified significant amount of cases not caught by experts.
从HCT治疗的血癌患者临床叙述中提取长期并发症的知识
生物医学本体支持的交互式信息提取(IE)系统是智能自然语言处理(NLP)工具,用于理解文献和临床叙述,并从非结构化文本中发现有意义的领域知识。本研究开发了集成的IE系统,用于在造血细胞移植(HCT)后的长期随访(LTFU)协议中从电子病历(EMR)中检测血癌患者的治疗并发症。与领域专家手动审查的金标准数据集相比,所提出的方法的性能非常令人鼓舞。此外,NLP系统还发现了大量专家没有发现的病例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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