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- W3100347911 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." @default.
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- W3100347911 date "2020-11-16" @default.
- W3100347911 modified "2023-09-26" @default.
- W3100347911 title "Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease" @default.
- W3100347911 cites W116902681 @default.
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- W3100347911 doi "https://doi.org/10.48550/arxiv.2011.08356" @default.
- W3100347911 hasPublicationYear "2020" @default.
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