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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Electronic Health Records (990) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (548) Secondary Use of Clinical Data for Research and Surveillance (341)

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 (2) Tweetations (14) 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 View