Using Unsupervised Machine Learning to Identify Subgroups Among Home Health Patients With Heart Failure Using Telehealth
Using Unsupervised Machine Learning to Identify Subgroups Among Home Health Patients With Heart Failure Using Telehealth
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DOI：
10.1097/cin.0000000000000423
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发表时间：
2018-05-01
影响因子：
1.3
通讯作者：
Radhakrishnan, Kavita
中科院分区：
文献类型：
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作者：
Bose, Eliezer;Radhakrishnan, Kavita
这项研究探索了使用无监督机器学习来识别在家庭健康环境中使用远程医疗服务的心力衰竭患者亚组，并检查了与病史，症状，药物，心理社会评估和医疗保健利用相关的患者特征的群集间差异。使用特征选择算法，我们从557例患者中选择了7个变量进行聚类。我们测试了三种聚类技术：分层，k-means和分割中心。层次聚类被确定为使用内部验证方法的最佳技术。采用（2）检验或单因素方差分析评估患者特征和结局之间的群间差异。范围从153到233例患者，三个集群显示出在年龄、性别、共病病史、β受体阻滞剂使用和生活质量评估等患者特征方面存在显著差异（P <0.05）的模式。在药物治疗、合并症和医疗保健利用方面也显示出显著的群间差异（P <0.001）。该研究确定了（1）心理健康状况，肺部疾病和肥胖之间的关联模式，以及（2）在家庭健康环境中使用远程医疗的心力衰竭患者的医疗保健利用率。研究结果还显示，老年女性比例最高的亚组缺乏指南推荐的心力衰竭处方药。
This study explored the use of unsupervised machine learning to identify subgroups of patients with heart failure who used telehealth services in the home health setting, and examined intercluster differences for patient characteristics related to medical history, symptoms, medications, psychosocial assessments, and healthcare utilization. Using a feature selection algorithm, we selected seven variables from 557 patients for clustering. We tested three clustering techniques: hierarchical, k-means, and partitioning around medoids. Hierarchical clustering was identified as the best technique using internal validation methods. Intercluster differences among patient characteristics and outcomes were assessed with either (2) test or one-way analysis of variance. Ranging in size from 153 to 233 patients, three clusters displayed patterns that differed significantly (P < .05) in patient characteristics of age, sex, medical history of comorbid conditions, use of beta blockers, and quality of life assessment. Significant (P < .001) intercluster differences in number of medications, comorbidities, and healthcare utilization were also revealed. The study identified patterns of association between (1) mental health status, pulmonary disorders, and obesity, and (2) healthcare utilization for patients with heart failure who used telehealth in the home health setting. Study results also revealed a lack of prescription guideline-recommended heart failure medications for the subgroup with the highest proportion of older female adults.