Due to the wider availability of modern electronic health records (EHR), patient care data is often being stored in the form of time-series. Clustering such timeseries data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines that are tailored to homogeneous patient subgroups. In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e.g., adverse events, the onset of comorbidities, etc.). The clustering is carried out by using our novel loss functions that encourage each cluster to have homogeneous future outcomes. We adopt actorcritic models to allow "back-propagation" through the sampling process that is required for assigning clusters to time-series inputs. Experiments on two realworld datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks and identifies meaningful clusters that can be translated into actionable information for clinical decision-making.
This paper proposes a temporal clustering algorithm for the medical domain. The main advantage of the proposed method is that it uses supervised information for temporal clustering. The proposed method is evaluated on two real-world datasets and showed improvements against a few other temporal clustering methods.
Detailed Comments: 
Methodology:
The actor-critic part of the loss is really just a type of policy gradient. There is no estimation of a value function in Eqn. 7 or anywhere to be found in Appendix B. This is very misleading on the part of the authors because policy grad and AC are very different algorithms. AC type of algorithms provide some variance reduction mechanisms for the classical policy gradient (not done here), but they require additional work to estimate a value function for future trajectories that incur bias. If the authors claim a real AC algorithm for the predictive clustering loss, then some justification for bias-variance tradeoffs should be mentioned instead of attributing it to tuning the hyperparameters between the losses.  
Perhaps a bigger issue is that there is a general tradeoff between reconstructing the time-series (the unsupervised learning portion) vs. predictive performance that is common to predictive clustering problems -- it is not addressed here. One can increase the performance in one (in this case prediction accuracy) while sacrificing the other. 
In the extreme case, one can just tune the hyperparameter of the loss function to turn this into a pure supervised learning problem while sacrificing the capacity of the embedding representations to have any meaning (e.g., to reconstruct time-series [SOM-VAE] or predict future ones). The authors did not really propose a systematic way to control this tradeoff, nor did they provide some experiments to show how well the embeddings can be used to recover the temporal patterns. 
Experiments:
The baseline experiments are rather weak. For example, 3 out of the 4 models (DTW, DCN extensions and SOM-VAE) were all unsupervised learning techniques that are not designed to take into account label information. This is especially true for comparison against SOM-VAE (which the authors called state of the art for the proposed problem). SOM-VAE is actually less intended for prognostication than the likes of Baytas et al. (2017) and Madiraju et al. (2018) and provides less informative baselines. The authors should also provide more details regarding how these ``extensions'' are done for things like DCN and K-means clustering on deep neural networks.