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Wang L, Fu S, Wen A, Ruan X, He H, Liu S, Moon S, Mai M, Riaz IB, Wang N, Yang P, Xu H, Warner JL, Liu H. Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing. JCO Clin Cancer Inform 2022; 6:e2200006. [PMID: 35917480 PMCID: PMC9470142 DOI: 10.1200/cci.22.00006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/18/2022] [Accepted: 06/15/2022] [Indexed: 11/20/2022] Open
Abstract
PURPOSE The 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. METHODS Published 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. RESULTS A 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. CONCLUSION We 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.
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Affiliation(s)
- Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Xiaoyang Ruan
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Huan He
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Sijia Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Michelle Mai
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - Irbaz B. Riaz
- Department of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ
| | - Nan Wang
- Department of Computer Science and Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Ping Yang
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX
| | - Jeremy L. Warner
- Departments of Medicine (Hematology/Oncology), Vanderbilt University, Nashville, TN
- Department Biomedical Informatics, Vanderbilt University, Nashville, TN
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
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Srabanti S, Tran M, Achim V, Fuller D, Canahuate G, Miranda F, Marai G. A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care. IEEE PACIFIC VISUALIZATION SYMPOSIUM : [PROCEEDINGS]. IEEE PACIFIC VISUALISATION SYMPOSIUM 2022; 2022:101-110. [PMID: 35928055 PMCID: PMC9344952 DOI: 10.1109/pacificvis53943.2022.00019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The annual incidence of head and neck cancers (HNC) worldwide is more than 550,000 cases, with around 300,000 deaths each year. However, the incidence rates and disease-characteristics of HNC differ between treatment centers and different populations, due to undetermined reasons, which may or not include socioeconomic factors. The multi-faceted and multi-variate nature of the data in the context of the emerging field of health disparities research makes automated analysis impractical. Hence, we present a visual analysis approach to explore the health disparities in the data of HNC patients from two different cohorts at two cancer care centers. Our approach integrates data from multiple sources, including census data and city data, with custom visual encodings and with a nearest neighbor approach. Our design, created in collaboration with oncology experts, makes it possible to analyze the patients' demographic, disease characteristics, treatments and outcomes, and to make significant comparisons of these two cohorts and of individual patients. We evaluate this approach through two case studies performed with domain experts. The results demonstrate that this visual analysis approach successfully accomplishes the goal of comparing two cohorts in terms of different significant factors, and can provide insights into the main source of health disparities between the two centers.
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Olex AL, McInnes BT. Review of Temporal Reasoning in the Clinical Domain for Timeline Extraction: Where we are and where we need to be. J Biomed Inform 2021; 118:103784. [PMID: 33862232 DOI: 10.1016/j.jbi.2021.103784] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/07/2021] [Accepted: 04/08/2021] [Indexed: 11/16/2022]
Abstract
Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried in their Electronic Health Record (EHR) in the form of unstructured clinical notes. This results in care providers spending time reading notes in a patient's record in order to become familiar with their condition prior to developing a diagnosis or treatment plan. Valuable time could be saved if this information was readily accessible for searching and visualization for fast comprehension by the medical team. Clinical Natural Language Processing (NLP) is an area of research that aims to build computational methods to automatically extract medically relevant information from unstructured clinical texts. A key component of Clinical NLP is Temporal Reasoning, as understanding a patient's medical history relies heavily on the ability to identify, assimilate, and reason over temporal information. In this work, we review the current state of Temporal Reasoning in the clinical domain with respect to Clinical Timeline Extraction. While much progress has been made, the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Areas such as handling relative and implicit temporal expressions, both in normalization and in identifying temporal relationships, improving co-reference resolution, and building inter-operable timeline extraction tools that can integrate multiple types of data are in need of new and innovative solutions to improve performance on clinical data.
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Affiliation(s)
- Amy L Olex
- Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA.
| | - Bridget T McInnes
- Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA
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Warner JL, Klemm JD. Informatics Tools for Cancer Research and Care: Bridging the Gap Between Innovation and Implementation. JCO Clin Cancer Inform 2020; 4:784-786. [PMID: 32870722 DOI: 10.1200/cci.20.00086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Juli D Klemm
- National Institutes of Health, National Cancer Institute, Bethesda, MD
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