Habib Alrashedi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Habib Alrashedi has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Amal Ali Alasmari
Nursing Administration and Education Department, Faculty of Nursing, University of Tabuk, Tabuk City 71491, Saudi Arabia
1 shared publication - Analita Gonzales
Nursing Administration and Education Department, Faculty of Nursing, University of Tabuk, Tabuk City 71491, Saudi Arabia
1 shared publication - Eddieson Pasay-An
Nursing Administration and Education Department, College of Nursing, King Khalid University, Abha 61421, Saudi Arabia
1 shared publication - Fatmah Ahmed Alamoudi
Department of Nursing, Prince Sultan Military College of Health Sciences, Dhahran City, Saudi Arabia
1 shared publication - Hwayoung Cho
University of Florida, Gainesville, FL, USA
1 shared publication - Khulud Abudawood
College of Nursing-Jeddah, King Saud bin Abdulaziz University for Health Sciences, Jeddah 21423, Saudi Arabia
1 shared publication
Latest Publications
- Open Access
- 29 August 2025
- Published by MDPI AG in Biomedicines
- Vol. 13 (9) , 2111
Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review
Background: Patients with heart failure (HF) are at high risk of readmission, contributing to substantial healthcare costs. This study investigated machine learning (ML) approaches to predict HF readmissions. Methods: A systematic review was conducted using several medical databases, adhering to the PRISMA guidelines, to identify studies employing ML to predict HF readmissions. Three reviewers independently screened the articles and extracted data. Results: Twenty-two studies from six countries were included in this study. Some studies examined 30-day readmissions, whereas others assessed 90-day, 180-day, or 1- to 3-year readmissions. Fourteen studies used supervised learning algorithms, with area under the curve (AUC) values ranging from 0.70 to 0.99, and unsupervised algorithms had AUCs of 0.69 to 0.72. The average age of the patients was 73 years, with approximately equal numbers of males and females. Conclusions: ML can predict HF-related hospitalization across various time frames. Supervised ML approaches and the incorporation of clinical knowledge may enhance model performance. Collaboration between providers and data scientists is needed to improve patient outcomes and reduce costs by using more accurate predictive models.
- 29 August 2025
- Published by MDPI AG in Biomedicines
- Vol. 13 (9) , 2111
Open AccessPredictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review
Background: Patients with heart failure (HF) are at high risk of readmission, contributing to substantial healthcare costs. This study investigated machine learning (ML) approaches to predict HF readmissions. Methods: A systematic review was conducted using several medical databases, adhering to the PRISMA guidelines, to identify studies employing ML to predict HF readmissions. Three reviewers independently screened the articles and extracted data. Results: Twenty-two studies from six countries were included in this study. Some studies examined 30-day readmissions, whereas others assessed 90-day, 180-day, or 1- to 3-year readmissions. Fourteen studies used supervised learning algorithms, with area under the curve (AUC) values ranging from 0.70 to 0.99, and unsupervised algorithms had AUCs of 0.69 to 0.72. The average age of the patients was 73 years, with approximately equal numbers of males and females. Conclusions: ML can predict HF-related hospitalization across various time frames. Supervised ML approaches and the incorporation of clinical knowledge may enhance model performance. Collaboration between providers and data scientists is needed to improve patient outcomes and reduce costs by using more accurate predictive models.