Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models
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CARDIOVASCULAR DISEASE (R FORAKER, SECTION EDITOR)
Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models Aixia Guo 1
&
Michael Pasque 2 & Francis Loh 1 & Douglas L. Mann 3 & Philip R. O. Payne 1
Accepted: 23 October 2020 # The Author(s) 2020
Abstract Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expertcomparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies. Keywords Heart failure (HF) . Machine learning . Deep learning . Artificial intelligence . Readmission . Mortality
Introduction Cardiovascular diseases (CVDs), which cause over 18.9 million deaths globally each year, are the number 1 cause of death, responsible for approximately 31% of all health-related deaths worldwide [1, 2]. Heart failure (HF) accounts for a large portion of this CVD morbidity and mortality, as well as an equally large portion of related healthcare expense [2]. One in five people will develop HF in their lifetime, and about 50% of these HF patients will die within 5 years [3]. In the management of this This article is part of the Topical Collection on Cardiovascular Disease * Aixia Guo [email protected] 1
Institute for Informatics (I2), Washington University School of Medicine, Barnes-Jewish Hospital, 600 S. Taylor Avenue, Suite 102, St. Louis, MO 63110, USA
2
Department of Surgery, Division of Cardiothoracic Surgery, Washington University School of Medicine, Barnes-Jewish Hospital, St. Louis, MO, USA
3
Department of Internal Medicine, Washington University School of Medicine, Barnes-Jewish Hospital, St. Louis, MO, USA
expanding HF patient population, the accurate prediction of HF outcomes is critical to effective prevention and treatment, as well as to the reduction of the burdensome expenditure of related healthcare dollars. The importance of accurate outcome prediction is accentuated by the impact of HF readmissions, which will cost Medicare approximately 17 billion dollars expended on the approximately 20% of patients who are readmitted within 30 days of HF discharge [4]. Expansive implementation of the EHR ha
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