Predicting community-acquired pneumonia outcome using time series data and machine learning
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Background
Community-acquired pneumonia (CAP) is an acute respiratory condition associated with high mortality in adult populations and is potentially more serious in older patients. Accurate and consistently applied prediction of outcome may contribute to reduce in-hospital mortality. Currently, CAP outcomes are assessed with clinical scores like CURB65, based on signs and symptoms that are non-specific to the disease. Recent literature has shown that machine learning (ML) has the potential to improve outcome prediction, but the sparse and incomplete nature of the data present a challenge for the development of models that can be implemented clinically.
Methods
This study aimed to developed ML models that can support outcome prediction in hospital admissions with CAP using routinely collected and time-dependent data from Leicester hospitals. Thus, by modelling mortality prediction, and predicting URB65 on the third day of admission with the forecast of vital signs, implementing a methodology that explores how different characteristics involved in the training process influence the results of the predictions.
Results
Data comprised 9390 admissions in the training set, and 7892 in the validation set, for thirty-four clinical variables (fifteen time-dependent). Results of CAP mortality modelling reported AUC of 0.77 using a GRU model that was trained with the time series of vital signs and blood test. Results also showed improvement in models when balancing classes of the target variable in the training set, as well as improvement when using time dependent data. And importantly when predicting URB65 accuracy of 0.85 was obtained when modelled using GRU, when time series were processed using local scaling.
Conclusions
This approach might represent an opportunity to anticipate adverse outcomes. These results suggest that ML models utilising time series can have sizable impact in the prediction of CAP outcome, from many perspectives: Complementing currently applied scoring systems approaches like CURB65 in hospital settings, prediction of mortality or forecasting the severity of patients from vital signs that have shown correlation with CAP mortality. The models presented require further validation and development, although they present important indication for CAP mortality prediction.
