Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease

• URL: http://arxiv.org/abs/2011.08356v1
• Date: Tue, 17 Nov 2020 01:18:33 GMT
• Title: Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease
• Authors: Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu
• Abstract summary: We evaluate three different clustering models on a large hospital dataset of vital-sign observations from patients suffering from COPD. We propose novel modifications to deal with unevenly sampled time-series data and unbalanced class distribution to improve phenotype separation.
• Score: 3.1564542805009332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data. A common approach to these problems involves clustering patients time-series information such as vital sign observations) to determine dissimilar subgroups of the patient population. Most clustering methods assume time-invariance of vital-signs and are unable to provide interpretability in clusters that is clinically relevant, for instance, event or outcome information. In this work, we evaluate three different clustering models on a large hospital dataset of vital-sign observations from patients suffering from Chronic Obstructive Pulmonary Disease. We further propose novel modifications to deal with unevenly sampled time-series data and unbalanced class distribution to improve phenotype separation. Lastly, we discuss further avenues of investigation for models to learn patient subgroups with distinct behaviour and phenotype.

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