Improving the future of healthcare starts by better understanding the current
actual practices in hospitals. This motivates the objective of discovering
typical care pathways from patient data. Revealing homogeneous groups of care
pathways can be achieved through clustering. The difficulty in clustering care
pathways, represented by sequences of timestamped events, lies in defining a
semantically appropriate metric and clustering algorithms.
In this article, we adapt two methods developed for time series to time
sequences: the drop-DTW metric and the DBA approach for the construction of
averaged time sequences. These methods are then applied in clustering
algorithms to propose original and sound clustering algorithms for timed
sequences.
This approach is experimented with and evaluated on synthetic and real use
cases.

通过对患者数据的时间序列进行聚类算法，发现和理解医院中的典型护理路径，从而改进未来的医疗实践。本研究将时间序列的两种方法应用到聚类算法中，并通过实验评估在合成和真实应用案例上的效果。

时序聚类 —— 应用于护理路径分析

Clustering of timed sequences -- Application to the analysis of care  pathways

We propose to use precise estimators of mutual information (MI) to find least
dependent components in a linearly mixed signal. On the one hand this seems to
lead to better blind source separation than with any other presently available
algorithm. On the other hand it has the advantage, compared to other
implementations of `independent' component analysis (ICA) some of which are
based on crude approximations for MI, that the numerical values of the MI can
be used for:
(i) estimating residual dependencies between the output components;
(ii) estimating the reliability of the output, by comparing the pairwise MIs
with those of re-mixed components;
(iii) clustering the output according to the residual interdependencies.
For the MI estimator we use a recently proposed k-nearest neighbor based
algorithm. For time sequences we combine this with delay embedding, in order to
take into account non-trivial time correlations. After several tests with
artificial data, we apply the resulting MILCA (Mutual Information based Least
dependent Component Analysis) algorithm to a real-world dataset, the ECG of a
pregnant woman.
The software implementation of the MILCA algorithm is freely available at
this http URL

本文提出使用互信息的精确估算器来找到一个线性混合信号中的最独立的组件。比其他 “独立” 成分分析算法更好的盲源分离并且可用于估计输出之间的残留依赖性，使用最近提出的 k 最近邻算法和延迟嵌入进行时间序列分析，算法已在怀孕女性的心电图数据集上进行了测试并实现了可用软件程序。