PCN244 Supervised Machine Learning for Predicting Mortality in Acute Myeloid Leukemia Patients Using Electronic Health Record Data

Abstract

This study implements supervised machine learning (ML) to predict mortality in acute myeloid leukemia (AML) patients and determine the important features in this prediction. Patients were selected from a large US electronic health records database (Cerner Real-World Data) that contains over 87 million patients. We investigated the first visit for patients with an AML ICD-10 diagnosis code, inpatient stay, length of stay of at least 48 hours, and non-missing gender and age. Patient characteristics, hospital characteristics, Charlson Index, quick sequential organ failure assessment (qSOFA), interventions (e.g., mechanical ventilation) and lab values (e.g., minimum white blood cell count) were included in this analysis. Several ML algorithms were compared through 10-fold cross validation; the best performing algorithm was tuned and evaluated with a test dataset. Feature importance was extracted from the final model through permutation importance. There were 8,968 patients included in this study. The ML algorithms that were compared included (mean cross-validation accuracy ± cross-validation standard deviation): logistic regression (72.9% ± 1.6%); random forests (77.5% ± 1.5%); extreme gradient boosting (XGBoost) (78.0% ± 1.2%); k-nearest neighbors (70.8% ± 1.1%); support vector machines (75.8% ± 1.3%). XGBoost was selected for the final model and after hyperparameter tuning had a prediction accuracy of 80.0%. The final model had an F1 score of 0.52, an area under the receiver operator characteristic curve (AUC ROC) of 0.79, a precision of 0.68, and a recall of 0.42. The top five most important features in this prediction were mechanical ventilation, qSOFA, age, intensive care unit admission and minimum white blood cell count. Supervised ML performed well in predicting mortality in AML patients, while identifying the most important features. Similar ML algorithms may identify higher risk AML patients earlier in the hospital to support earlier efforts to modify routine management.