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

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.