基于复杂的网络社区发现算法,研究症状组成,时间序列演变和间歇性囊炎的网络可视化
Lei Pang1,2, Zijun Ding3, Xiaodong Bian1
1Urology Department of Shanxi Provincial People's Hospital, Taiyuan, Shanxi Province, China.
IET systems biology
|November 13, 2023
概括
这项研究使用网络分析分析了间歇性囊炎 (IC) 症状,确定了三个核心症状社区. 治疗显著降低了症状,特别是在非膀相关的群体中,为临床管理提供了洞察力.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 网络科学 网络科学
- 数据挖掘 数据挖掘
背景情况:
- 间歇性囊炎 (IC) 是一种复杂的疾病,具有多种症状.
- 了解症状演变和相互关系对于有效管理至关重要.
- 现有的研究往往缺乏对症状动态的全面分析.
研究的目的:
- 分析间歇性囊炎 (IC) 患者的症状组成和演变.
- 为了可视化症状网络并识别核心症状集群.
- 为IC的临床诊断和治疗策略提供数据驱动的参考.
主要方法:
- 利用复杂的网络社区发现算法和定向加权复杂网络.
- 分析了552名IC门诊患者的电子病例数据.
- 使用Sankey地图可视化治疗前后症状演变.
主要成果:
- 确定了三个核心症状群体:与膀相关的 (60%),非膀相关的1 (32%) 和非膀相关的2个 (8%).
- 治疗后观察到显著的症状减少,白天频率 (93.22%),急迫性 (93.07%) 和夜 (82.33%) 显著下降.
- 症状群体C显示治疗后降低率最高 (71.43%).
结论:
- 复杂的网络分析有效地揭示了间歇性囊炎 (IC) 症状模式和治疗结果.
- 症状社区为临床决策提供可解释的见解.
- 向治疗的有效性各不相同,饮食调节和神经调节在特定症状方面表现出高的成功率.
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