从多变量纵向临床数据中识别阿尔茨海默病的风险因素,使用时间模式挖掘
Annette Spooner1, Gelareh Mohammadi2, Perminder S Sachdev3
1School of Computer Science and Engineering, UNSW Sydney, Sydney, Australia. a.spooner@unsw.edu.au.
BMC bioinformatics
|February 18, 2025
概括
我们开发了临床时间模式挖掘 (C-TPM),这是分析复杂患者数据的高效框架. C-TPM识别了高风险的时间模式,可以预测阿尔茨海默氏症等疾病,有助于早期检测和了解疾病进展.
科学领域:
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
- 医疗信息学医学信息学
背景情况:
- 临床数据对建模具有挑战,原因是异质性和时间序列的多样性.
- 现有的时间模式挖掘算法没有针对复杂的临床数据集进行优化.
- 了解疾病发病和进展需要有效建模患者纵向信息.
研究的目的:
- 引入一个高效和可扩展的框架,即临床时间模式挖掘 (C-TPM),用于对现实世界的临床数据进行时间模式挖掘.
- 从多变量,纵向患者数据中识别临床相关和高风险的时间模式.
- 提高临床数据分析的计算效率和可解释性.
主要方法:
- 开发了C-TPM,集成时间抽象,扩展的TPMiner算法,相对风险,赔率比率和多处理.
- 应用了一套完整的截止值,用于数据的离散和解释.
- 使用可视化模块来清晰地表示模式.
主要成果:
- 将C-TPM应用于阿尔茨海默病 (AD) 数据集,发现了预测AD的模式.
- 在使用发现模式的生存分析模型中获得了高的一致性指数 (高达0.87).
- 在发现的时间模式中确定了临床相关的变量.
结论:
- C-TPM提供了一种有效和可扩展的方法来建模复杂的纵向临床数据.
- 该框架可以识别罕见或进展缓慢的疾病的模式.
- 对于各种医疗和非临床数据领域,C-TPM是可通用的.
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