From population- to patient-based prediction of in-hospital mortality in heart failure using machine learning
Author:
Affiliation:
1. Heart Center Leipzig at University of Leipzig, Department of Electrophysiology , Strümpellstraße 39, 04289 Leipzig , Germany
2. Leipzig Heart Institute , Leipzig , Germany
3. Helios Hospitals , Berlin , Germany
4. Helios Health , Berlin , Germany
Abstract
Publisher
Oxford University Press (OUP)
Subject
Energy Engineering and Power Technology,Fuel Technology
Link
https://academic.oup.com/ehjdh/advance-article-pdf/doi/10.1093/ehjdh/ztac012/43461079/ztac012.pdf
Reference10 articles.
1. Evaluating risk prediction models for adults with heart failure: a systematic literature review;Di Tanna;PLoS One,2020
2. Risk prediction in patients with heart failure: a systematic review and analysis;Rahimi;JACC Heart Fail,2014
3. Machine learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure;König;ESC Heart Fail,2021
4. In-hospital care in acute heart failure during the COVID-19 pandemic: insights from the German-wide Helios hospital network;Konig;Eur J Heart Fail,2020
5. Derivation and validation of an in-hospital mortality prediction model suitable for profiling hospital performance in heart failure;Lagu;J Am Heart Assoc,2018
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1. Adopting artificial intelligence in cardiovascular medicine: a scoping review;Hypertension Research;2023-10-31
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