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Recent years have seen an increased focus into the tasks of predictinghospital inpatient risk of deterioration and trajectory evolution due to theavailability of electronic patient data. A common approach to these problemsinvolves clustering patients time-series information such as vital signobservations) to determine dissimilar subgroups of the patient population. Mostclustering methods assume time-invariance of vital-signs and are unable toprovide interpretability in clusters that is clinically relevant, for instance,event or outcome information. In this work, we evaluate three differentclustering models on a large hospital dataset of vital-sign observations frompatients suffering from Chronic Obstructive Pulmonary Disease. We furtherpropose novel modifications to deal with unevenly sampled time-series data andunbalanced class distribution to improve phenotype separation. Lastly, wediscuss further avenues of investigation for models to learn patient subgroupswith distinct behaviour and phenotype.

Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease