Published on in Vol 20, No 11 (2018): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10497, first published .
Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application

Journals

  1. Zhou L, Suominen H, Gedeon T. Adapting State-of-the-Art Deep Language Models to Clinical Information Extraction Systems: Potentials, Challenges, and Solutions. JMIR Medical Informatics 2019;7(2):e11499 View
  2. Walters C, Nitin R, Margulis K, Boorom O, Gustavson D, Bush C, Davis L, Below J, Cox N, Camarata S, Gordon R. Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems. Journal of Speech, Language, and Hearing Research 2020;63(9):3019 View
  3. Senders J, Cho L, Calvachi P, McNulty J, Ashby J, Schulte I, Almekkawi A, Mehrtash A, Gormley W, Smith T, Broekman M, Arnaout O. Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma. JCO Clinical Cancer Informatics 2020;(4):25 View
  4. Ferrario A, Demiray B, Yordanova K, Luo M, Martin M. Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning. Journal of Medical Internet Research 2020;22(9):e19133 View
  5. Zhou P, He Y, Lyu C, Yang X. Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach. Vaccines 2020;8(4):691 View
  6. Le Glaz A, Haralambous Y, Kim-Dufor D, Lenca P, Billot R, Ryan T, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. Journal of Medical Internet Research 2021;23(5):e15708 View
  7. Kohli M, Kar A, Sinha S. The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review. IEEE Access 2022;10:104887 View
  8. Noori A, Magdamo C, Liu X, Tyagi T, Li Z, Kondepudi A, Alabsi H, Rudmann E, Wilcox D, Brenner L, Robbins G, Moura L, Zafar S, Benson N, Hsu J, R Dickson J, Serrano-Pozo A, Hyman B, Blacker D, Westover M, Mukerji S, Das S. Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study. Journal of Medical Internet Research 2022;24(8):e40384 View
  9. Lam S, Fang A, Koh M, Shantakumar S, Yeo S, Matchar D, Ong M, Poon K, Huang L, Harikrishan S, Milea D, Burke D, Webb D, Ragavendran N, Tan N, Loo C. Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration. BMC Medical Informatics and Decision Making 2023;23(1) View
  10. Ward P, Young A, Slavova S, Liford M, Daniels L, Lucas R, Kavuluru R. Deep Neural Networks for Fine-Grained Surveillance of Overdose Mortality. American Journal of Epidemiology 2023;192(2):257 View
  11. Marquardt K. Physician Practice Style for Mental Health Conditions: The Case of ADHD. SSRN Electronic Journal 2021 View
  12. Ben-Sasson A, Guedalia J, Nativ L, Ilan K, Shaham M, Gabis L. A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning. Children 2024;11(4):429 View

Books/Policy Documents

  1. Polpinij J, Kachai T, Nasomboon K, Bheganan P. Recent Advances in Information and Communication Technology 2019. View
  2. Henry S, Yetisgen M, Uzuner O. Mental Health Informatics. View