Competing risk modelling for in-hospital length of stay
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In this study, we propose a framework for analysing in-hospital patient data from electronic health records. We transform longitudinal sparse vital signs measurements into cross-sectional data via descriptive statistics, imputing missing values, and evaluating variables strongly associated with time to mutually exclusive events (favourable medical discharge or deterioration). We employ competing risk and random survival forest techniques to predict patients’ length of stay and evaluate models’ performance via Brier score.