ArticleJournal of pathology informatics2017
Application of Text Information Extraction System for Real-Time Cancer Case Identification in an Integrated Healthcare Organization.
Article in Journal of pathology informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07406087 (Adaptation and Implementation of an Evidence-Based Patient Navigation Intervention for Adolescent and Young Adult Cancer Survivors), which is not on this map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Adaptation and Implementation of an Evidence-Based Patient Navigation Intervention for Adolescent and Young Adult Cancer Survivors
Who cites it
13 citing papers in PubMed.
- Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study.JMIR formative research · 2023Article
- A cross-institutional evaluation on breast cancer phenotyping NLP algorithms on electronic health records.Computational and structural biotechnology journal · 2023Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system.BMC medical research methodology · 2022Article
- Improving Precision of Do Not Contact Codes: Results of a Manual Review to Inform Coding and Case Contact Procedures.Journal of registry management · 2022Review
- Limitations of Transformers on Clinical Text Classification.IEEE journal of biomedical and health informatics · 2021Article
- Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text.JCO clinical cancer informatics · 2020Article
- Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.Journal of pathology informatics · 2020Article
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Review
- deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Article
- Integration of Cancer Registry Data into the Text Information Extraction System: Leveraging the Structured Data Import Tool.Journal of pathology informatics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical pathology reports (SPR) contain rich clinical diagnosis information. The text information extraction system (TIES) is an end-to-end application leveraging natural language processing technologies and focused on the processing of pathology and/or radiology reports.
methodsWe deployed the TIES system and integrated SPRs into the TIES system on a daily basis at Kaiser Permanente Southern California. The breast cancer cases diagnosed in December 2013 from the Cancer Registry (CANREG) were used to validate the performance of the TIES system. The National Cancer Institute Metathesaurus (NCIM) concept terms and codes to describe breast cancer were identified through the Unified Medical Language System Terminology Service (UTS) application. The identified NCIM codes were used to search for the coded SPRs in the back-end datastore directly. The identified cases were then compared with the breast cancer patients pulled from CANREG.
resultsA total of 437 breast cancer concept terms and 14 combinations of "breast"and "cancer" terms were identified from the UTS application. A total of 249 breast cancer cases diagnosed in December 2013 was pulled from CANREG. Out of these 249 cases, 241 were successfully identified by the TIES system from a total of 457 reports. The TIES system also identified an additional 277 cases that were not part of the validation sample. Out of the 277 cases, 11% were determined as highly likely to be cases after manual examinations, and 86% were in CANREG but were diagnosed in months other than December of 2013.
conclusionsThe study demonstrated that the TIES system can effectively identify potential breast cancer cases in our care setting. Identified potential cases can be easily confirmed by reviewing the corresponding annotated reports through the front-end visualization interface. The TIES system is a great tool for identifying potential various cancer cases in a timely manner and on a regular basis in support of clinical research studies.
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