加强基于无监督机器学习患者聚类的药剂师干预准.
Chi Chun Steve Tsang1, Junling Wang1
1Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, TN, USA.
药剂师可以识别需要糖尿病护理干预的患者. 机器学习确定了特定的患者群体,包括公开保险的老年人和私人保险的中年女性,以获得有针对性的支持,以改善遵守美国糖尿病协会标准.
科学领域:
- 糖尿病管理 糖尿病管理
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的机器学习
背景情况:
- 遵守美国糖尿病协会 (ADA) 医疗保健标准是不理想的.
- 有效地识别患者进行糖尿病干预对于改善健康结果至关重要.
研究的目的:
- 帮助药剂师识别患者进行糖尿病控制干预.
- 应用无监督机器学习,用于糖尿病护理中的患者分层.
主要方法:
- 对2021年医疗支出小组调查数据的分析.
- 利用了对患者特征的k模式集群分析,包括预防性护理的坚持和人口统计数据.
- 包括遵守HbA1c测试,脚部检查,胆固醇测试,眼睛检查和流感疫苗接种.
主要成果:
- 包括1219名自报糖尿病患者; ADA标准的整体遵守率为33.72%.
- 根据复杂性,保险和人口统计数据确定了五个患者集群.
- 集群B (中等复杂性,公共保险女性),C (低复杂性,私人保险女性) 和E (中等复杂性,公共保险男性) 显示不遵守.
结论:
- 药剂师可以针对特定的患者群体进行糖尿病干预.
- 公共保险的老年人 (B组和E组) 和私人保险的中年女性 (C组) 是主要目标.
- 干预措施可以包括资源导航和提醒,以改善糖尿病护理的遵守.
更多相关视频
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
相关概念视频
Analysis of Population Pharmacokinetic Data
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Drug Therapy
Antianxiety Medications
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
Nonlinear Pharmacokinetics: Overview
Nonlinearity can arise due to the saturation of plasma protein-binding or...
