Citations 2
Evaluation of mCODE Coverage in EHR: a Scoping Review of Cancer Natural Language Processing
Wang, Liwei , Ruan, Xiaoyang , Mai, Michelle , Wang, Nan , Xu, Hua , Fu, Sunyang , Wen, Andrew , He, Huan , Liu, Sijia , Moon, Sungrim , Riaz, Irbaz Bin , Yang, Ping , Warner, Jeremy L. , Liu, Hongfang
2022 IEEE 10th International Conference on Healthcare Informatics (ICHI) | September 8, 2022 | Volume: No volume available | Issue: No issue available | Pages: 517-518
Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing
Wang, Liwei , Fu, Sunyang , Wen, Andrew , Ruan, Xiaoyang , Liu, Sijia , Moon, Sungrim , Mai, Michelle , Riaz, Irbaz Bin , Wang, Nan , Xu, Hua , He, Huan , Liu, Sijia , Moon, Sungrim , Riaz, Irbaz B , Yang, Ping , Warner, Jeremy L , Liu, Hongfang , Fu, Sunyang , Wen, Andrew , He, Huan , Yang, Ping , Warner, Jeremy L. , Liu, Hongfang
JCO Clinical Cancer Informatics | August 2, 2022 | Volume: No volume available | Issue: 6 | Pages: No page available
PURPOSEThe advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.METHODSPublished literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.RESULTSA total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.CONCLUSIONWe conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.