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

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