伴随性疾病进展分析:使用时间性伴随性疾病网络进行患者分层和伴随性疾病预测
Ye Liang1, Chonghui Guo1, Hailin Li2,3
1Institute of Systems Engineering, Dalian University of Technology, Dalian, Liaoning China.
Health information science and systems
|September 16, 2024
概括
本研究引入了一个框架,用于分析使用时间性共同发病网络 (TCN) 的共同发病进展. 它有助于识别患者子组并预测未来的并发症,改善医疗保健洞察力.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 了解并发症进展对于有效的患者管理和个性化治疗策略至关重要.
- 现有的方法往往缺乏捕捉疾病共发生的动态时间模式的能力.
- 确定不同的人口特异性并发症轨迹对于及时干预至关重要.
研究的目的:
- 开发和验证一个框架来分析使用时间性共同疾病网络 (TCN) 的共同疾病进展模式.
- 为了能够及时检测潜在的并发症,并提高对并发症状况发展的理解.
- 根据患者的并发症进展情况,将患者分为不同的亚组.
主要方法:
- 从患者纵向诊断数据构建时间性并发症网络 (TCN).
- 通过初步和处方分析利用TCN进行患者分层.
- 开发了一个与距离匹配的时间性并发症网络 (TCN-DM) 通过识别相似的患者来预测并发症.
主要成果:
- 该框架成功地使用MIMIC-III数据集识别了四个不同的心力衰竭 (HF) 亚组.
- 在这些HF患者亚组中,TCN揭示了显著的并发症进展模式.
- 在并发症预测方面,TCN-DM方法的表现优于其他方法,F1得分在0.454和0.612之间.
结论:
- 拟议的框架有效地识别了特定人群的并发症模式,并预测了未来的并发症发展.
- 这种方法为个人患者护理和人口健康管理提供了宝贵的见解.
- 在临床环境中,TCN-DM方法有望提高并发症预测的准确性.
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