在不平衡的数据上挖掘与糖尿病相关的并发病的基于时间的模式:一个诊断前后的研究
Róbert Bata1, Amr Sayed Ghanem1, Eszter Vargáné Faludi1
1Department of Epidemiology, Faculty of Health Sciences, University of Debrecen, Debrecen, Hungary.
BMC medical informatics and decision making
|September 30, 2025
概括
2型糖尿病 (T2DM) 的管理是复杂的,因为伴随性疾病. 协会规则挖矿发现高血压,病和呼吸系统疾病通常在T2DM诊断后出现,指导向治疗.
科学领域:
- 内分泌学和新陈代谢学
- 在医疗保健中的数据科学.
- 公共卫生 公共卫生
背景情况:
- 2型糖尿病 (T2DM) 影响全球超过5.29亿成年人,预计到2050年将达到13亿.
- T2DM经常与多种并发症共存,使疾病管理复杂化,增加发病率和死亡率,并挑战治疗疗效.
研究的目的:
- 探索T2DM诊断之前和之后的并发症的流行率和时间模式.
- 使用协会规则挖掘 (ARM) 和网络可视化来分析患者数据.
主要方法:
- 对来自匈牙利德布雷森大学临床中心25065名患者的不平衡数据的分析.
- 协会规则挖掘 (ARM) 的应用,以确定T2DM和并发症之间的关系.
- 网络可视化,以说明疾病出现的时间模式.
主要成果:
- 在T2DM诊断后的头五年内,新出现的健康状况显著增加.
- 高血压通常在早期出现,而肺炎,眼睛疾病和缺血性心脏病在整个疾病进展过程中持续存在.
- ARM确定急性和慢性病和呼吸系统疾病是T2DM后常见的并发症. 男性的心力衰竭和急性损伤率更高.
结论:
- ARM技术有效地揭示了慢性疾病管理中的复杂模式,特别是T2DM并发症.
- 这些发现表明了开发有针对性的干预和个性化治疗策略的潜在途径.
- 了解并发症模式可以改善T2DM患者的治疗结果和医疗管理.
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