TY - JOUR AU - Fränti, Pasi AU - Sieranoja, Sami AU - Wikström, Katja AU - Laatikainen，Tiina PY - 2022 DA - 2022/5/4 TI -聚类诊断病人从5800万年访问芬兰在2015年和2018年之间乔-地中海JMIR通知SP - e35422六世- 10 - 5 KW - multimorbidity KW -聚类分析KW -疾病同现KW - multimorbidity网络KW -医疗数据分析KW -图聚类KW - k - means KW -数据分析KW -集群KW -机器学习KW -合并症KW -注册KW -大数据KW -芬兰KW -欧洲KW -健康记录AB -背景:患者的多种慢性疾病是卫生服务系统的一大负担。目前，疾病大多是分开治疗，没有充分重视疾病之间的关系，导致护理过程支离破碎。更好地整合服务可以导致更有效地组织整个卫生保健系统。目的:本研究旨在根据疾病共发情况分析疾病之间的联系，以支持决策者更好地组织卫生保健服务。方法:我们使用来自芬兰卫生保健登记的初级和专门卫生保健访问和住院护理的数据，对诊断进行了聚类分析。本研究的目标人群包括2015年至2018年使用医疗保健服务的380万名≥18岁的个体(3,835,531/5,487,308，占总人口的69.90%)。他们总共有5800万次访问。基于诊断的共发性进行聚类。 The more the same pair of diagnoses appeared in the records of the same patients, the more the diagnoses correlated with each other. On the basis of the co-occurrences, we calculated the relative risk of each pair of diagnoses and clustered the data by using a graph-based clustering algorithm called the M-algorithm—a variant of k-means. Results: The results revealed multimorbidity clusters, of which some were expected (eg, one representing hypertensive and cardiovascular diseases). Other clusters were more unexpected, such as the cluster containing lower respiratory tract diseases and systemic connective tissue disorders. The annual cost of all clusters was €10.0 billion, and the costliest cluster was cardiovascular and metabolic problems, costing €2.3 billion. Conclusions: The method and the achieved results provide new insights into identifying key multimorbidity groups, especially those resulting in burden and costs in health care services. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2022/5/e35422 UR - https://doi.org/10.2196/35422 UR - http://www.ncbi.nlm.nih.gov/pubmed/35507390 DO - 10.2196/35422 ID - info:doi/10.2196/35422 ER -