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

Joon Myoung Kwon, Kyung Hee Kim, Ki Hyun Jeon, Sang Eun Lee, Hae Young Lee, Hyun Jai Cho, Jin Oh Choi, Eun Seok Jeon, Min Seok Kim, Jae Joong Kim, Kyung Kuk Hwang, Shung Chull Chae, Sang Hong Baek, Seok Min Kang, Dong Ju Choi, Byung Su Yoo, Kye Hun Kim, Hyun Young Park, Myeong Chan Cho, Byung Hee Oh

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


Aims This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). Methods and results 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 machinelearning 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<0.001). Conclusion DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.

Original languageEnglish
Article numbere0219302
JournalPloS one
Issue number7
Publication statusPublished - 2019 Jul 1

Bibliographical note

Publisher Copyright:
© 2019 Kwon et al.

All Science Journal Classification (ASJC) codes

  • General


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