Doctor of Philosophy, Case Western Reserve University, 0, Nursing
Acute myocardial infarction (AMI) remains one of the most common causes of death in the United States and allocates a tremendous amount of healthcare expenditures that go beyond $12 billion annually. Machine learning (ML), a subset of artificial intelligence, has emerged as valuable methodological tool to advance nursing and biomedical research. The ML has shown to build predictive models that allow detection of risk factors, assist in diagnosis, and propose personalized treatments plans that may lead to enhanced patients' outcomes. From this perspective, the purpose of this study was to develop and evaluate the predictive performance of a random forest machine-learning model with a conventional multivariate logistic regression model established to examine the influence of individuals predisposing, enabling, and need factors on health service use outcomes (in-hospital mortality, 30-day readmission) of AMI patients guided by the Andersen Model (2008). The cross-sectional retrospective study utilized the Medical Information Mart for Intensive Care which comprises patients admitted to a large tertiary-level academic center of the Harvard Medical School in Boston, MA. The variables of interest include age, gender, ethnicity, type of insurance, body mass index, existing comorbidities, in-hospital mortality, 30-day readmission. Patients with a primary diagnosis of ST-segment elevation MI and non-ST-segment elevation MI cared for at emergency department or critical care units were included in the study. Predictive models for each health service use outcomes were built using RStudio. There were a total of 1171 AMI patients included in the study, 255 (21.8%) patients with STEMI and 916 (78.2%) patients with NSTEMI. Predictors of in-hospital mortality and 30-day readmission included age and existing comorbidities. The accuracy rate, sensitivity, specificity, and AUC for the random forest models were 68-75%, 72-81%, 41-50%, and 0.58-0.59, respectively. On the other hand, the a (open full item for complete abstract)
Committee: Ronald Hickman Jr., PhD, RN, ACNP-BC, FNAP, FAAN (Committee Chair); Mary Dolansky PhD, RN, FAAN (Committee Member); Nicholas Schiltz PhD (Committee Member); Richard Josephson MD, MS, FACC, FAHA, FACP, FAACVPR (Committee Member)
Subjects: Nursing