Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospital...

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Bibliographic Details
Published in:PloS one 2019-07, Vol.14 (7), p.e0219302-e0219302
Main Authors: Kwon, Joon-Myoung, Kim, Kyung-Hee, Jeon, Ki-Hyun, Lee, Sang Eun, Lee, Hae-Young, Cho, Hyun-Jai, Choi, Jin Oh, Jeon, Eun-Seok, Kim, Min-Seok, Kim, Jae-Joong, Hwang, Kyung-Kuk, Chae, Shung Chull, Baek, Sang Hong, Kang, Seok-Min, Choi, Dong-Ju, Yoo, Byung-Su, Kim, Kye Hun, Park, Hyun-Young, Cho, Myeong-Chan, Oh, Byung-Hee
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Algorithms
Area Under Curve
Artificial Intelligence
Big Data
Biology and Life Sciences
Cardiology
Chronic Disease
Comparative analysis
Computer and Information Sciences
Computer centers
Confidence intervals
Congestive heart failure
Datasets
Deep Learning
Female
Forecasting - methods
Health aspects
Heart failure
Heart Failure - mortality
Hospital Mortality - trends
Hospitals
Humans
Internal medicine
Kaplan-Meier Estimate
Learning algorithms
Machine learning
Male
Medical prognosis
Medical research
Medicine
Medicine and Health Sciences
Methods
Middle Aged
Mortality
Neural networks
Patient outcomes
Patients
Peptides
Performance tests
Physical Sciences
Prognosis
Registries
Republic of Korea - epidemiology
Research and Analysis Methods
Risk
Risk Assessment - methods
Risk Factors
Risk groups
ROC Curve
South Korea
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Summary:This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines-Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876-0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720-0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0219302