Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24008, first published .
Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition

Journals

  1. Kim Y, Heider P, Lally I, Meystre S. A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System. JMIR Medical Informatics 2021;9(4):e22797 View
  2. Rybinski M, Dai X, Singh S, Karimi S, Nguyen A. Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis. JMIR Medical Informatics 2021;9(4):e24020 View
  3. Hao T, Huang Z, Liang L, Weng H, Tang B. Health Natural Language Processing: Methodology Development and Applications. JMIR Medical Informatics 2021;9(10):e23898 View
  4. Clift K, Macklin-Mantia S, Barnhorst M, Millares L, King Z, Agarwal A, Presutti R. Comparison of a Focused Family Cancer History Questionnaire to Family History Documentation in the Electronic Medical Record. Journal of Primary Care & Community Health 2022;13:215013192110697 View
  5. Sathish Kumar L, Routray S, Prabu A, Rajasoundaran S, Pandimurugan V, Mukherjee A, Al-Numay M. Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models. Cluster Computing 2023;26(4):2325 View
  6. Shi J, Morgan K, Bradshaw R, Jung S, Kohlmann W, Kaphingst K, Kawamoto K, Fiol G. 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. JMIR Medical Informatics 2022;10(8):e37842 View
  7. Gao Y, Dligach D, Christensen L, Tesch S, Laffin R, Xu D, Miller T, Uzuner O, Churpek M, Afshar M. A scoping review of publicly available language tasks in clinical natural language processing. Journal of the American Medical Informatics Association 2022;29(10):1797 View
  8. Chen J, Zhang H, Yang M, Wang R, Zhang H, Ren Z, Wang Q, Liu Y, Chen J, Ji J, Zhao J, He G, Guo J, Zhu K, Yang X, Ma H, Wang C, Huang J. Genomic formation of Tibeto-Burman speaking populations in Guizhou, Southwest China. BMC Genomics 2023;24(1) View
  9. Wang L, He H, Wen A, Moon S, Fu S, Peterson K, Ai X, Liu S, Kavuluru R, Liu H. Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers–Assisted Sublanguage Analysis. JMIR Medical Informatics 2023;11:e48072 View
  10. 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

Books/Policy Documents

  1. García-Santa N, Cetina K. Machine Learning and Knowledge Discovery in Databases. View