ArticleJMIR medical informatics2022
Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Review
- Economic Evaluation of GARDE: A Digital Health Platform for Population-Level Hereditary Cancer Risk Assessment.JCO oncology practice · 2026Article
- Electronic health records-based algorithms to screen for U.S. Centers for Disease Control and Prevention tier 1 genetic diseases: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Validation of a guidelines-based digital tool to assess the need for germline cancer genetic testing.Hereditary cancer in clinical practice · 2024Article
- Screening Familial Risk for Hereditary Breast and Ovarian Cancer.JAMA network open · 2024Article
- Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own.Journal of biomedical informatics · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
backgroundFamily health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive.
objectiveThe aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP.
methodsAlgorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast, ovarian, pancreatic, and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR.
resultsRegarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria.
conclusionsCompared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.
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