Derivation and validation of a machine learning-based risk prediction model in patients with acute heart failure-Reference-Cited by-同舟云学术

Derivation and validation of a machine learning-based risk prediction model in patients with acute heart failure

Published:2023-06 Issue:6 Volume:81 Page:531-536
ISSN:0914-5087
Container-title:Journal of Cardiology
language:en
Short-container-title:Journal of Cardiology

Author:

Misumi Kayo,Matsue Yuya,Nogi Kazutaka,Fujimoto Yudai,Kagiyama Nobuyuki,Kasai Takatoshi,Kitai Takeshi,Oishi Shogo,Akiyama Eiichi,Suzuki Satoshi,Yamamoto Masayoshi,Kida Keisuke,Okumura Takahiro,Nogi Maki,Ishihara Satomi,Ueda Tomoya,Kawakami Rika,Saito Yoshihiko,Minamino Tohru

Funder

Cardiovascular Research Fund, Tokyo

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine

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1. Integrating Structured and Unstructured Data with BERTopic and Machine Learning: A Comprehensive Predictive Model for Mortality in ICU Heart Failure Patients;Applied Sciences;2024-08-26

2. Usefulness of hypochloremia at the time of discharge to predict prognosis in patients with chronic heart failure after hospitalization;Journal of Cardiology;2024-08

3. Development of interpretable machine learning models to predict in‐hospital prognosis of acute heart failure patients;ESC Heart Failure;2024-05-15

4. Positive association between sodium-to-chloride ratio and in-hospital mortality of acute heart failure;Scientific Reports;2024-04-03

5. Advanced Machine Learning for Risk Assessment of Cardiovascular Disease;2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS);2023-11-24