| Popis: |
Supplemental oxygen is an essential part of in-hospital care for most patients hospitalized with SARS-CoV-2 pneumonia. This study seeks to identify hospitalized patients who will require advanced oxygen support (high flow oxygen, CPAP, BiPAP, or mechanical ventilation) within 96 hours of admission using a machine learning model. This information can be useful for hospitals to plan for nurse staffing, as patients will consume more resources and will need more staff assistance. Data from 302 SARS-CoV-2 patients was used to create a classifier to predict whether or not patients would require advanced oxygen support within 96 hours of admission. Of the 302 cases, 211 were randomly selected to train the model, and 91 were randomly selected for testing. Through a labeled dataset, we performed supervised learning by using a random forest ensemble model which included demographic, clinical comorbidities, vitals, and laboratory values. We used 5-fold cross-validation to evaluate our trained model and employed a majority vote decision across the five trained models in order to produce the final prediction for a given patient. Through the models, we yielded results through sensitivity, specificity, positive predictive value, negative predictive value, and F1 score with the 91 cases of training data. An additional 24 cases were used to test the validity of the ensemble consensus model. Approximately 40% of all patients progressed to require advanced oxygen support 96 hours after the initial presentation. Although the insight gained from the model may not definitively predict the course of an individual patient, this model may help hospital administrators plan for staffing needs with a 48- hour lead time. Patients on high oxygen support require high acuity beds, which have increased nurse-topatient ratios. Additional samples may increase its statistical significance. Nevertheless, this model demonstrates the potential and viability of using data science to help manage hospital resources. |
