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

Natural Language Processing (650) New Methods (911) Theme Issue 2021: Health Natural Language Processing and Applications (16) Cost-Effectiveness and Economics (234) Secondary Use of Clinical Data for Research and Surveillance (325) Clinical Information and Decision Making (1339) Decision Support for Health Professionals (1145) Clinical Informatics (918) Health Services Research (413) Machine Learning (1400)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31442, first published .
Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

Authors of this article:

Raghav Ramachandran1 Author Orcid Image ;   Michael J McShea1 Author Orcid Image ;   Stephanie N Howson1 Author Orcid Image ;   Howard S Burkom1 Author Orcid Image ;   Hsien-Yen Chang2 Author Orcid Image ;   Jonathan P Weiner2 Author Orcid Image ;   Hadi Kharrazi2 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Cowley H, Robinette M, Matelsky J, Xenes D, Kashyap A, Ibrahim N, Robinson M, Zeger S, Garibaldi B, Gray-Roncal W. Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients. Scientific Reports 2023;13(1) View
  2. Howson S, McShea M, Ramachandran R, Burkom H, Chang H, Weiner J, Kharrazi H. Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology. JMIR Medical Informatics 2022;10(3):e33212 View