Knowledge Extraction of Long-Term Complications from Clinical Narratives of Blood Cancer Patients with HCT Treatments
Pages 525 - 526
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.
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Index Terms
- Knowledge Extraction of Long-Term Complications from Clinical Narratives of Blood Cancer Patients with HCT Treatments
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Published In
August 2018
727 pages
ISBN:9781450357944
DOI:10.1145/3233547
- General Chairs:
- Amarda Shehu,
- Cathy Wu,
- Program Chairs:
- Christina Boucher,
- Jing Li,
- Hongfang Liu,
- Mihai Pop
Copyright © 2018 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 15 August 2018
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- the Leukemia & Lymphoma Society Scholar Award for Clinical Research (S Armenian)
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BCB '18
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BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
August 29 - September 1, 2018
DC, Washington, USA
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BCB '18 Paper Acceptance Rate 46 of 148 submissions, 31%;
Overall Acceptance Rate 254 of 885 submissions, 29%
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