Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts.

中文翻译：

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深度学习对缺失值的多变量临床患者轨迹进行聚类

精准医学需要根据疾病表现对患者进行分层，提供足够的信息以便根据每个患者选择治疗方法。对于许多疾病，例如神经系统疾病，这种分层问题转化为多变量聚类和相对较短时间序列的复杂问题，因为（i）这些疾病是多因素的，不能用单一临床结果变量很好地描述，（ii）疾病进展需要随着时间的推移受到监控。此外，临床数据通常还受到许多缺失值的存在的阻碍，从而使任何聚类尝试进一步复杂化。