PloS one, 2019-07, Vol.14 (7), p.e0219302-e0219302
2019
Volltextzugriff (PDF)
Details
- Autor(en) / Beteiligte
- Titel
- Artificial intelligence algorithm for predicting mortality of patients with acute heart failure
- Ist Teil von
- PloS one, 2019-07, Vol.14 (7), p.e0219302-e0219302
- Ort / Verlag
- United States: Public Library of Science
- Erscheinungsjahr
- 2019
- Quelle
- Elektronische Zeitschriftenbibliothek (Open access)
- Beschreibungen/Notizen
- 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<0.001). 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1932-6203eISSN: 1932-6203DOI: 10.1371/journal.pone.0219302Titel-ID: cdi_plos_journals_2254081872
- Format
- –
- Schlagworte
- 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