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首页> 外文期刊>BMC Medical Informatics and Decision Making >AliClu - Temporal sequence alignment for clustering longitudinal clinical data
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AliClu - Temporal sequence alignment for clustering longitudinal clinical data

机译：Aliclu - 延时临床数据的时间序列对齐

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Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient’s current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierarchical clustering. To find the best number of clusters and assess their stability, a resampling technique is applied. We propose the AliClu, a novel tool for clustering temporal clinical data based on the TNW algorithm coupled with clustering validity assessments through bootstrapping. The AliClu was applied for the analysis of the rheumatoid arthritis EMRs obtained from the Portuguese database of rheumatologic patient visits (Reuma.pt). In particular, the AliClu was used for the analysis of therapy switches, which were coded as letters corresponding to biologic drugs and included their durations before each change occurred. The obtained optimized clusters allow one to stratify the patients based on their temporal therapy profiles and to support the identification of common features for those groups. The AliClu is a promising computational strategy to analyse longitudinal patient data by providing validated clusters and by unravelling the patterns that exist in clinical outcomes. Patient stratification is performed in an automatic or semi-automatic way, allowing one to tune the alignment, clustering, and validation parameters. The AliClu is freely available at https://github.com/sysbiomed/AliClu.
机译：患者分层是临床决策中的关键任务，因为它可以允许医生以个性化的方式选择治疗。鉴于具有纵向数据的电子医疗记录（EMRS）的可用性越来越多，一个至关重要的问题是如何根据医疗约会的时间信息如何有效地聚集患者。在这项工作中，我们建议应用时间需求人员-Wunsch（TNW）算法将离散序列与符号之间的转换时间信息对齐。这些符号可以对应于患者的当前治疗，其整体健康状况或任何其他离散状态。转换时间信息表示这些状态的每个持续时间。然后使用获得的TNW成对分数来执行分层聚类。为了找到最佳数量的群集并评估其稳定性，应用重采样技术。我们提出了一种基于TNW算法的用于聚类时间临床数据的新型工具，通过自引导耦合到聚类有效性评估。 Aliclu用于分析从风湿病患者访问葡萄牙数据库（REUMA.PT）中获得的类风湿性关节炎EMR。特别地，Aliclu用于治疗开关的分析，其被编码为对应于生物药物的字母，并在每次变化之前包括它们的持续时间。所获得的优化簇允许基于其临时治疗型材对患者进行分层，并支持识别这些群体的共同特征。 Aliclu是一种有前途的计算策略，用于通过提供验证的群集来分析纵向患者数据，并通过解开临床结果中存在的模式来分析纵向患者数据。患者分层以自动或半自动方式执行，允许一个调整对齐，聚类和验证参数。 Aliclu在HTTPS://github.com/sysbiomed/aliclu自由提供。

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