Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing

Abstract

The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model-using only triage priorities-with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission.

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19

“…We used admission vitals instead of time series data. Consistent with our decision on using admission vitals for disease stratification, Fernandes and colleagues used ML and natural language processing algorithm to predict ICU admission among patients presenting to ER [ 19 ]. As with our results in COVID-19 patients, they noted initial vitals including heart rate, oxygen saturation, RR and sBP to be highly correlated to ICU admission.…”

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

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Prognostic machine learning models for COVID‐19 to facilitate decision making

“…6 Another similar machine learning model based on hospital data from a Portuguese and American hospital was able to predict the risk of ICU admission. 7 A study from Denmark using a machine learning model was able to predict 90-day mortality for intensive care unit patients using time series data. 8 The key findings of this model were that the predictive performance significantly improved over the These examples underscore the capabilities of machine learning.…”

“…Using a longitudinal dataset of electronic health records (EHR) from more than 700 000 patients, a machine learning model was able to predict future acute kidney injury 6 . Another similar machine learning model based on hospital data from a Portuguese and American hospital was able to predict the risk of ICU admission 7 . A study from Denmark using a machine learning model was able to predict 90‐day mortality for intensive care unit patients using time series data 8 .…”