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AliClu - Temporal sequence alignment for clustering longitudinal clinical data.
BMC Medical Informatics and Decision Making ( IF 3.5 ) Pub Date : 2019-12-30 , DOI: 10.1186/s12911-019-1013-7
Kishan Rama 1, 2 , Helena Canhão 3 , Alexandra M Carvalho 1 , Susana Vinga 2
Affiliation  

BACKGROUND 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. RESULTS 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. CONCLUSIONS 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.

中文翻译:

AliClu-时间序列比对,用于对纵向临床数据进行聚类。

背景技术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方法。鉴于具有纵向数据的电子病历(EMR)的可用性不断提高,一个关键问题是如何根据来自医疗约会的时间信息有效地对患者进行聚类。在这项工作中,我们建议应用时间Needoral Needleman-Wunsch(TNW)算法将离散序列与符号之间的转换时间信息对齐。这些符号可能对应于患者的当前治疗方法,其总体健康状况或任何其他离散状态。过渡时间信息表示每个状态的持续时间。然后,将获得的TNW成对得分用于执行分层聚类。为了找到最佳数量的群集并评估其稳定性,应用了重采样技术。结果我们提出AliClu,这是一种基于TNW算法对时态临床数据进行聚类的新型工具,并通过自举进行聚类有效性评估。AliClu用于分析从葡萄牙风湿病患者就诊数据库(Reuma.pt)获得的类风湿关节炎EMR。特别是,AliClu用于分析治疗开关,其编码为对应于生物药物的字母,并包括每次发生变化之前的持续时间。所获得的优化聚类允许人们根据他们的时间疗法概况对患者进行分层,并支持识别这些组的共同特征。结论AliClu是一种有前途的计算策略,可通过提供经过验证的聚类并通过揭示临床结果中存在的模式来分析纵向患者数据。以自动或半自动方式执行患者分层,从而允许调整对齐,聚类和验证参数。可从https://github.com/sysbiomed/AliClu免费获得AliClu。
更新日期:2019-12-31
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