Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

(2020) Davide Chicco et al.   BMC Medical Informatics and Decision Making

Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan

(2020) Chien-An Hu et al.   BMJ Open

Physician Prediction versus Model Predicted Prognosis in Ambulatory Patients with Heart Failure

(2019) T.A. Buchan et al.   JOURNAL OF HEART AND LUNG TRANSPLANTATION

Efficient partition of integer optimization problems with one-hot encoding

(2019) Shuntaro Okada et al.   Scientific Reports

Machine Learning Prediction of Mortality and Hospitalization in Heart Failure with Preserved Ejection Fraction

(2019) Suveen Angraal et al.   JACC-Heart Failure

Age affects the prognostic impact of diabetes in chronic heart failure

(2018) Filipe Manuel Cunha et al.   ACTA DIABETOLOGICA

Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association

(2018) Emelia J. Benjamin et al.   CIRCULATION

Predicting diabetes-related hospitalizations based on electronic health records

(2018) Theodora S Brisimi et al.   STATISTICAL METHODS IN MEDICAL RESEARCH

Predicting Diabetes Mellitus With Machine Learning Techniques

(2018) Quan Zou et al.   Frontiers in Genetics

Unintended Consequences of Machine Learning in Medicine

(2017) Federico Cabitza et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Points of Significance: Ensemble methods: bagging and random forests

(2017) Naomi Altman et al.   NATURE METHODS

Global mortality variations in patients with heart failure: results from the International Congestive Heart Failure (INTER-CHF) prospective cohort study

(2017) Hisham Dokainish et al.   Lancet Global Health

Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure

(2017) Jarrod D. Frizzell et al.   JAMA Cardiology

Machine Learning and Data Mining Methods in Diabetes Research

(2017) Ioannis Kavakiotis et al.   Computational and Structural Biotechnology Journal

Analysis of Machine Learning Techniques for Heart Failure Readmissions

(2016) Bobak J. Mortazavi et al.  

2016 ACC/AHA/HFSA Focused Update on New Pharmacological Therapy for Heart Failure: An Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure

(2016) Clyde W. Yancy et al.   JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY

Analysis of Machine Learning Techniques for Heart Failure Readmissions

(2016) Bobak J. Mortazavi et al.   Circulation-Cardiovascular Quality and Outcomes

A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

(2015) Kennedy Were et al.   ECOLOGICAL INDICATORS

Risk Prediction in Patients With Heart Failure

(2014) Kazem Rahimi et al.   JACC-Heart Failure

MissForest--non-parametric missing value imputation for mixed-type data

(2011) D. J. Stekhoven et al.   BIOINFORMATICS

Learning from Imbalanced Data

(2009) Haibo He et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

A robust approach based on conditional value-at-risk measure to statistical learning problems

(2008) Akiko Takeda et al.   EUROPEAN JOURNAL OF OPERATIONAL RESEARCH