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

Natural Language Processing (650) Clinical Informatics (919) Electronic Health Records (971) Machine Learning (1401) Theme Issue 2020:National NLP Clinical Challenges/Open Health Natural Language Processing 2019 Challenge Selected Papers (17)

Published on in Vol 8, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21750, first published .
Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

Authors of this article:

Hong-Jie Dai1, 2, 3 Author Orcid Image ;   You-Qian Lee1 Author Orcid Image ;   Chandini Nekkantti4 Author Orcid Image ;   Jitendra Jonnagaddala5 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Journals

  1. 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
  2. ALLA N, CHEN A, BATONGBACAL S, NEKKANTTI C, Dai H, JONNAGADDALA J. Cohort selection for construction of a clinical natural language processing corpus. Computer Methods and Programs in Biomedicine Update 2021;1:100024 View
  3. Binsfeld Gonçalves L, Nesic I, Obradovic M, Stieltjes B, Weikert T, Bremerich J. Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame. JMIR Medical Informatics 2022;10(12):e40534 View
  4. Suissa O, Zhitomirsky-Geffet M, Elmalech A, Alam M, de Boer V, Daga E, van Erp M, Hyvönen E, Peñuela A, Sack H, Hyvonen E, Meroño-Peñuela A. Question answering with deep neural networks for semi-structured heterogeneous genealogical knowledge graphs. Semantic Web 2022;14(2):209 View
  5. Zanotto B, Beck da Silva Etges A, dal Bosco A, Cortes E, Ruschel R, De Souza A, Andrade C, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120 View
  6. Suissa O, Zhitomirsky-geffet M, Elmalech A. Around the GLOBE: Numerical Aggregation Question-answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks. Journal on Computing and Cultural Heritage 2023;16(3):1 View