Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

<h4>Objective</h4>Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory...

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Autores principales:	Aixia Guo, Nikhilesh R Mazumder, Daniela P Ladner, Randi E Foraker
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8be2fe0f334f46a7bcf30c01ab784a2d
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spelling	oai:doaj.org-article:8be2fe0f334f46a7bcf30c01ab784a2d2021-12-02T20:19:19ZPredicting mortality among patients with liver cirrhosis in electronic health records with machine learning.1932-620310.1371/journal.pone.0256428https://doaj.org/article/8be2fe0f334f46a7bcf30c01ab784a2d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256428https://doaj.org/toc/1932-6203<h4>Objective</h4>Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality.<h4>Materials and methods</h4>We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models.<h4>Results</h4>Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined.<h4>Conclusion</h4>Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.Aixia GuoNikhilesh R MazumderDaniela P LadnerRandi E ForakerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256428 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aixia Guo Nikhilesh R Mazumder Daniela P Ladner Randi E Foraker Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
description	<h4>Objective</h4>Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality.<h4>Materials and methods</h4>We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models.<h4>Results</h4>Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined.<h4>Conclusion</h4>Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.
format	article
author	Aixia Guo Nikhilesh R Mazumder Daniela P Ladner Randi E Foraker
author_facet	Aixia Guo Nikhilesh R Mazumder Daniela P Ladner Randi E Foraker
author_sort	Aixia Guo
title	Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
title_short	Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
title_full	Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
title_fullStr	Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
title_full_unstemmed	Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
title_sort	predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8be2fe0f334f46a7bcf30c01ab784a2d
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_version_	1718374239251202048

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

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