Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.

Journal: Expert review of pharmacoeconomics & outcomes research

PMID: 39311657

Abstract

OBJECTIVES: Adherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.

Authors

Chi Chun Steve Tsang

Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, TN, USA.
Junling Wang

School of Journalism & Communication, Lanzhou University, Lanzhou, Gansu 730000, China.

Keywords

Adult Age Factors Aged Cluster Analysis Diabetes Complications Diabetes Mellitus Female Humans Hypoglycemic Agents Insurance, Health Machine Learning Male Middle Aged Pharmaceutical Services Pharmacists Professional Role Self Report Sex Factors Surveys and Questionnaires United States Unsupervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (39311657)

Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.

Abstract

Authors

Keywords

External Resources

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