This paper is in the following e-collection/theme issue:

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Electronic Health Records (1271) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (679) Secondary Use of Clinical Data for Research and Surveillance (452)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37842, first published .
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

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

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

Authors of this article:

Jianlin Shi1, 2, 3 Author Orcid Image ;   Keaton L Morgan3, 4 Author Orcid Image ;   Richard L Bradshaw3 Author Orcid Image ;   Se-Hee Jung3, 5 Author Orcid Image ;   Wendy Kohlmann6, 7 Author Orcid Image ;   Kimberly A Kaphingst7, 8 Author Orcid Image ;   Kensaku Kawamoto3 Author Orcid Image ;   Guilherme Del Fiol3 Author Orcid Image
Article Authors Cited by (8) Tweetations (13) Metrics

Journals

  1. Bradshaw R, Kawamoto K, Bather J, Goodman M, Kohlmann W, Chavez-Yenter D, Volkmar M, Monahan R, Kaphingst K, Del Fiol G. 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 2024;149:104568 View
  2. Wang L, Rahimi Larki N, Dobkin J, Salgado S, Ahmad N, Kaplan D, Yang W, Yang Y. A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With Acute Pancreatitis. Pancreas 2024;53(3):e254 View
  3. Kiser D, Elhanan G, Bolze A, Neveux I, Schlauch K, Metcalf W, Cirulli E, McCarthy C, Greenberg L, Grime S, Blitstein J, Plauth W, Grzymski J. Screening Familial Risk for Hereditary Breast and Ovarian Cancer. JAMA Network Open 2024;7(9):e2435901 View
  4. Russell C, Daley A, Van Arnem D, Hila A, Johnson K, Davies J, Cytron H, Ready K, Armstrong C, Sylvester M, Caleshu C. Validation of a guidelines-based digital tool to assess the need for germline cancer genetic testing. Hereditary Cancer in Clinical Practice 2024;22(1) View
  5. Bilal M, Hamza A, Malik N. NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review. Journal of Pain and Symptom Management 2025;69(5):e374 View
  6. Stammers M, Ramgopal B, Owusu Nimako A, Vyas A, Nouraei R, Metcalf C, Batchelor J, Shepherd J, Gwiggner M. A foundation systematic review of natural language processing applied to gastroenterology & hepatology. BMC Gastroenterology 2025;25(1) View
  7. Liang E, Pei S, Staibano P, van der Woerd B. Clinical applications of large language models in medicine and surgery: A scoping review. Journal of International Medical Research 2025;53(7) View
  8. Harris W, Hernandez M, Ngo K, Fladger A, Brunette C, Hamarneh S, Knowles J, Lebo M, Vassy J. 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 2025 View