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Gigascience. 2019 Nov 1;8(11). pii: giz134. doi: 10.1093/gigascience/giz134.

Deep learning for clustering of multivariate clinical patient trajectories with missing values.

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UCB Biosciences GmbH, Alfred-Nobel-Strasse 10, 40789 Monheim, Germany.
Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Konrad-Adenauer-Strasse, 53754 Sankt Augustin, Germany.
Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany.
UCB Pharma, Bath Road 216, Slough SL1 3WE, UK.
UCB Pharma, Chemin du Foriest 1, 1420 Braine-l'Alleud, Belgium.
Erasmus MC, University Medical Center Rotterdam, Department of Radiology, Doctor Molewaterplein 40, PO Box 2040, 3000 CA Rotterdam, Netherlands.
Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Department of Medical Informatics, PO Box 2040, 3000 CA Rotterdam, Netherlands.



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.


The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease.


We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general.


clustering; deep learning; multivariate longitudinal data; multivariate time series; patient stratification

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