A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals

• URL: http://arxiv.org/abs/2006.05514v1
• Date: Tue, 9 Jun 2020 21:21:38 GMT
• Title: A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals
• Authors: Jhonatan Kobylarz, Henrique D. P. dos Santos, Felipe Barletta, Mateus Cichelero da Silva, Renata Vieira, Hugo M. P. Morales, Cristian da Costa Rocha
• Abstract summary: Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled.
• Score: 4.659599449441919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. The challenging task of clinical deterioration identification in hospitals lies in the intense daily routines of healthcare practitioners, in the unconnected patient data stored in the Electronic Health Records (EHRs) and in the usage of low accuracy scores. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness and could thus assist the healthcare team. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled. In this work we used 121,089 medical encounters from six different hospitals and 7,540,389 data points, and we compared popular ward protocols with six different scalable machine learning methods (three are classic machine learning models, logistic and probabilistic-based models, and three gradient boosted models). The results showed an advantage in AUC (Area Under the Receiver Operating Characteristic Curve) of 25 percentage points in the best Machine Learning model result compared to the current state-of-the-art protocols. This is shown by the generalization of the algorithm with leave-one-group-out (AUC of 0.949) and the robustness through cross-validation (AUC of 0.961). We also perform experiments to compare several window sizes to justify the use of five patient timestamps. A sample dataset, experiments, and code are available for replicability purposes.

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