Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16848, first published .
Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Journals

  1. Wan P, Satybaldy A, Huang L, Holtskog H, Nowostawski M. Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and Proposed Framework (MedAlert). Journal of Medical Internet Research 2020;22(10):e22013 View
  2. Kim K, Yang H, Yi J, Son H, Ryu J, Kim Y, Jeong J, Chin H, Na K, Chae D, Han S, Kim S. Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation. Journal of Medical Internet Research 2021;23(4):e24120 View
  3. Baron J, Huang R, McEvoy D, Dighe A. Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts. JAMIA Open 2021;4(1) View
  4. Mehta N, Born K, Fine B. How artificial intelligence can help us ‘Choose Wisely’. Bioelectronic Medicine 2021;7(1) View
  5. Ozaydin B, Berner E, Cimino J. Appropriate use of machine learning in healthcare. Intelligence-Based Medicine 2021;5:100041 View