Quality of life after the ICU: A machine learning approach to one-year post-ICU health predictions

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PURPOSE - Due to the increasing number of Intensive Care Unit (ICU) survivors, the long term outcomes of the survival of a serious condition become progressively more important. With predictive modeling, the consequences of the ICU stay in terms of quality of life (QoL) can be estimated. This study aimed to improve the statistical model that is currently in use by using machine learning models and additional patient data from the Electronic Health Records (EHR). METHODS - Data from the EHR was gathered from adult patients that were admitted to the ICU between June 2016 and January 2019. Several regression-based machine learning models, among which Random Forest and Support Vector Regression, were tted on a combination of patient-reported data and 71 expert-selected EHR variables in order to predict the target value of change in QoL one year after admission. Results were compared to a baseline model. RESULTS - Results from all tested regression models show that certain features that can be extracted from EHR data, such as high body temperatures and low BMIs, can have an in uence on the regression-based prediction scores. However, the improvements in target prediction caused by these additions are limited. The best performing model had a decrease of 0.004 in mean absolute error (MAE) compared to the baseline results. CONCLUSION - A machine learning-based approach to one-year post-ICU QoL prediction using EHR data was developed in this study. This approach had a small positive e ect on prediction results when compared to the established statistical model, but the distinction is too small to put the model into practice without having to compromise the practicability by physicians. Keywords Quality of life, Critical care, Survivors, Machine learning, Prediction modeling
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