30个潜在类别模型的特征
包含基于日本保险索赔数据构建的30个潜在类别模型参数，揭示多病模式对医疗费用和死亡率的影响，重点关注代谢综合征。
基本信息
资源简介
30个潜在类别模型的特征
【数据集背景】
本数据集源站标题为《Features of the 30 latent class models.》。源站首次发布：2023-09-28。
【数据内容】
源站原始描述：Although the economic burden of multimorbidity is a growing global challenge, the contribution of multimorbidity in patients with high medical expenses remains unclear. We aimed to clarify multimorbidity patterns that have a large impact on medical costs in the Japanese population. We conducted a cross-sectional study using health insurance claims data provided by the Japan Health Insurance Association. Latent class analysis (LCA) was used to identify multimorbidity patterns in 1,698,902 patient…
【数据结构与技术规格】
文件数 1 个，合计 17.92 KB。数据集 DOI：10.1371/journal.pone.0291554.t003。
【主题与分类】
源站学科分类：Medicine、Sociology、Biological Sciences not elsewhere classified、Science Policy。主题标签：xlink "> although、type 2 diabetes、growing global challenge、generalized linear model、latent class analysis、7 classes based、working age population、total medical costs、total medical cost、identify multimorbidity patterns。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Features_of_the_30_latent_class_models_/24213648
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Nishida, Yuki; Anzai, Tatsuhiko; Takahashi, Kunihiko; Kozuma, Takahide; Kanda, Eiichiro; Yamauchi, Keita; et al. (2023). Features of the 30 latent class models.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Features of the 30 latent class models.
- DOI：10.1371/journal.pone.0291554.t003
- 发布日期：2023-09-28
- 文件数：1 个
- 文件总体积：17.92 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Features_of_the_30_latent_class_models_/24213648
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/42499074"
文件清单
Table 3.xls（17.92 KB）
文件总体积 17.92 KB（源站 API 实测）。
数据说明
Although the economic burden of multimorbidity is a growing global challenge, the contribution of multimorbidity in patients with high medical expenses remains unclear. We aimed to clarify multimorbidity patterns that have a large impact on medical costs in the Japanese population. We conducted a cross-sectional study using health insurance claims data provided by the Japan Health Insurance Association. Latent class analysis (LCA) was used to identify multimorbidity patterns in 1,698,902 patients who had the top 10% of total medical costs in 2015. The present parameters of the LCA model included 68 disease labels that were frequent among this population. Moreover, subgroup analysis was performed using a generalized linear model (GLM) to assess the factors influencing annual medical cost and 5-year mortality. As a result of obtaining 30 latent classes, the kidney disease class required the most expensive cost per capita, while the highest portion (28.6%) of the total medical cost was spent on metabolic syndrome (MetS) classes, which were characterized by hypertension, dyslipidemia, and type 2 diabetes. GLM applied to patients with MetS classes showed that cardiovascular diseases or complex conditions, including malignancies, were powerful determinants of medical cost and mortality. MetS was classified into 7 classes based on real-world data and accounts for a large portion of the total medical costs. MetS classes with cardiovascular diseases or complex conditions, including malignancies, have a significant impact on medical costs and mortality.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0291554.t003（source: https://plos.figshare.com/articles/dataset/Features_of_the_30_latent_class_models_/24213648）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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