神经退行性疾病中的多种疾病:网络分析.
Mostafa Amini1, Ali Bagheri2, Martin P Paulus3,4
1Department of Information Systems, College of Business, California State University, Long Beach, California, USA.
Informatics for health & social care
|October 4, 2024
概括
伴随性疾病显著影响神经退行性疾病 (NDs) 的社会经济成本. 电子健康记录的网络分析确定了关键的共同情况,有助于更好的患者护理和资源分配.
科学领域:
- 医疗信息学 医疗信息学
- 网络科学 网络科学
- 公共卫生 公共卫生
背景情况:
- 神经退行性疾病 (NDs) 造成了相当大的社会经济负担.
- 伴随性疾病显著加剧了这些成本,并影响了患者的治疗结果.
- 了解疾病关联对于有效的医疗保健管理至关重要.
研究的目的:
- 通过网络分析分析与神经退行性疾病 (NDs) 相关的流行并发症.
- 为了确定经常与NDs同时发生的关键疾病.
- 为改善资源配置和患者护理提供一个框架.
主要方法:
- 从一个大型纵向电子健康记录 (EHR) 数据集 (93,647,498例诊断,824,847例患者) 中构建一个多病症网络 (MN).
- 应用Phi-相关性和Cosine指数 (CI) 来测量疾病关联.
- 利用网络中心性的措施来排名流行并发症.
- 创建了针对疾病患病率和强度的类级网络.
主要成果:
- 一般的MN包括928种疾病和337,253个协会.
- 99%的可信度网络显示了575种关系 (73种疾病通过Phi相关性,102种通过CI).
- 根据中心性指标,有13种疾病被确定为最显著的多重疾病.
- 对ND的具体分析确定了关键的相关条件和关系.
结论:
- 网络分析为理解神经退行性疾病 (NDs) 中复杂疾病并发症提供了强大的框架.
- 确定关键的多病症可以指导临床实践,预防策略和资源配置.
- 这种方法可以改善患者和护理人员的生活质量.
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