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Scientific paper - Original scientific paper
Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score
Diagnostics, 14 (2024), 10; 981. https://doi.org/10.3390/diagnostics14100981
Salkić, Nermin; Jovanović, Predrag; Barišić Jaman, Mislav; Selimović, Nedim; Paštrović, Frane; Grgurević, Ivica

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Salkić, N., Jovanović, P., Barišić Jaman, M., Selimović, N., Paštrović, F. & Grgurević, I. (2024). Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score. Diagnostics, 14. (10). doi: 10.3390/diagnostics14100981

Salkić, Nermin, et al. "Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score." Diagnostics, vol. 14, no. 10, 2024. https://doi.org/10.3390/diagnostics14100981

Salkić, Nermin, Predrag Jovanović, Mislav Barišić Jaman, Nedim Selimović, Frane Paštrović and Ivica Grgurević. "Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score." Diagnostics 14, no. 10 (2024). https://doi.org/10.3390/diagnostics14100981

Salkić, N., et al. (2024) 'Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score', Diagnostics, 14(10). doi: 10.3390/diagnostics14100981

Salkić N, Jovanović P, Barišić Jaman M, Selimović N, Paštrović F, Grgurević I. Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score. Diagnostics [Internet]. 2024 May 08 [cited 2025 February 12];14(10). doi: 10.3390/diagnostics14100981

N. Salkić, P. Jovanović, M. Barišić Jaman, N. Selimović, F. Paštrović and I. Grgurević, "Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score", Diagnostics, vol. 14, no. 10, May 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:105:949609. [Accessed: 12 February 2025]

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Metadata
Title (english)Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score
AuthorNermin Salkić
AuthorPredrag Jovanović
AuthorMislav Barišić Jaman
AuthorNedim Selimović
AuthorFrane Paštrović
AuthorIvica Grgurević
Author's institutionUniversity of Zagreb
School of Medicine
(Department of Internal Medicine)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Internal Medicine
Abstract (english)
Prediction of short-term mortality in patients with acute decompensation of liver cirrhosis could be improved. We aimed to develop and validate two machine learning (ML) models for predicting 28-day and 90-day mortality in patients hospitalized with acute decompensated liver cirrhosis. We trained two artificial neural network (ANN)-based ML models using a training sample of 165 out of 290 (56.9%) patients, and then tested their predictive performance against Model of End-stage Liver Disease-Sodium (MELD-Na) and MELD 3.0 scores using a different validation sample of 125 out of 290 (43.1%) patients. The area under the ROC curve (AUC) for predicting 28-day mortality for the ML model was 0.811 (95%CI: 0.714- 0.907; p < 0.001), while the AUC for the MELD-Na score was 0.577 (95%CI: 0.435–0.720; p = 0.226) and for MELD 3.0 was 0.600 (95%CI: 0.462–0.739; p = 0.117). The area under the ROC curve (AUC) for predicting 90-day mortality for the ML model was 0.839 (95%CI: 0.776- 0.884; p < 0.001), while the AUC for the MELD-Na score was 0.682 (95%CI: 0.575–0.790; p = 0.002) and for MELD 3.0 was 0.703 (95%CI: 0.590–0.816; p < 0.001). Our study demonstrates that ML-based models for predicting short-term mortality in patients with acute decompensation of liver cirrhosis perform significantly better than MELD-Na and MELD 3.0 scores in a validation cohort.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Journal titleDiagnostics
Numberingvol. 14, no. 10, 981
e-ISSN2075-4418
DOIhttps://doi.org/10.3390/diagnostics14100981
URN:NBNurn:nbn:hr:105:949609
Publication2024-05-08
Document URLhttps://www.mdpi.com/2075-4418/14/10/981
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2025-02-05 14:33:21