Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer.

Journal: JCO clinical cancer informatics

Published Date: Oct 1, 2019

Abstract

PURPOSE: Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand cancer survival outcomes. We developed a natural language processing (NLP) system to identify patient-specific timelines of metastatic breast cancer recurrence.

Authors

Imon Banerjee

Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
Selen Bozkurt

Department of Biostatistics and Medical Informatics, Akdeniz University Faculty of Medinice, 48000 Antalya, Turkey.
Jennifer Lee Caswell-Jin

Stanford University School of Medicine, Stanford, CA.
Allison W Kurian

Department of Medicine, Stanford University School of Medicine, Stanford, CA.
Daniel L Rubin

Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

Keywords

Adult Aged Algorithms Breast Neoplasms California Databases, Factual Decision Support Systems, Clinical Female Follow-Up Studies Humans Medical Informatics Middle Aged Natural Language Processing Neoplasm Recurrence, Local Registries Time Factors

External Resources

View on PubMed Access via DOI PubMed (31584836)

Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer.

Abstract

Authors

Keywords

External Resources

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