In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

Sazzli Kasim, Sorayya Malek, Cheen Song, Wan Azman Wan Ahmad, Alan Fong, Khairul Shafiq Ibrahim, Muhammad Shahreeza Safiruz, Firdaus Aziz, Jia Hui Hiew and Nurulain Ibrahim

PLOS ONE, 2022, vol. 17, issue 12, 1-28

Abstract: Background: Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006–2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278944

DOI: 10.1371/journal.pone.0278944

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