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Artificial intelligence algorithm for predicting mortality of patients with acute heart failure
  • 작성일2020-05-07
  • 최종수정일2020-05-07
  • 담당부서연구기획과
  • 연락처043-719-8033
  • 308

PLoS One, 2019. 14(7), e0219302-, DOI: https://doi.org/10.1371/journal.pone.0219302


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


Abstract

    Aims: This study aimed to develop and validate deep-learning-based artificial intelligence algorithmfor predicting mortality of AHF (DAHF).

    Methods and results: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data forDAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolledto 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 Getwith the Guidelines–Heart Failure (GWTG-HF) score, Meta-Analysis Global Group inChronic Heart Failure (MAGGIC) score, and other machine-learning models by using thetest 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 significantlyoutperformed those of the GWTG-HF (0.728 [0.720–0.737]) and other machinelearningmodels. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantlyoutperformed 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 thelow-risk group(p<0.001).

    Conclusion: DAHF predicted the in-hospital and long-term mortality of patients with AHF more accuratelythan the existing risk scores and other machine-learning models.



  • 본 연구는 질병관리본부 연구개발과제연구비를 지원받아 수행되었습니다.
  • This research was supported by a fund by Research of Korea Centers for Disease Control and Prevention.


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