Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approach...

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Autores principales:	Oliver Haas, Andreas Maier, Eva Rothgang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	in-hospital mortality critical care odds ratio associative classification machine learning artificial intelligence Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/1eeed6ee220448a6aad391d4e27c93b8
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spelling	oai:doaj.org-article:1eeed6ee220448a6aad391d4e27c93b82021-11-08T05:54:13ZRule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care2296-858X10.3389/fmed.2021.785711https://doaj.org/article/1eeed6ee220448a6aad391d4e27c93b82021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.785711/fullhttps://doaj.org/toc/2296-858XWe propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.Oliver HaasOliver HaasAndreas MaierEva RothgangFrontiers Media S.A.articlein-hospital mortalitycritical careodds ratioassociative classificationmachine learningartificial intelligenceMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	in-hospital mortality critical care odds ratio associative classification machine learning artificial intelligence Medicine (General) R5-920
spellingShingle	in-hospital mortality critical care odds ratio associative classification machine learning artificial intelligence Medicine (General) R5-920 Oliver Haas Oliver Haas Andreas Maier Eva Rothgang Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
description	We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.
format	article
author	Oliver Haas Oliver Haas Andreas Maier Eva Rothgang
author_facet	Oliver Haas Oliver Haas Andreas Maier Eva Rothgang
author_sort	Oliver Haas
title	Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
title_short	Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
title_full	Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
title_fullStr	Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
title_full_unstemmed	Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
title_sort	rule-based models for risk estimation and analysis of in-hospital mortality in emergency and critical care
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/1eeed6ee220448a6aad391d4e27c93b8
work_keys_str_mv	AT oliverhaas rulebasedmodelsforriskestimationandanalysisofinhospitalmortalityinemergencyandcriticalcare AT oliverhaas rulebasedmodelsforriskestimationandanalysisofinhospitalmortalityinemergencyandcriticalcare AT andreasmaier rulebasedmodelsforriskestimationandanalysisofinhospitalmortalityinemergencyandcriticalcare AT evarothgang rulebasedmodelsforriskestimationandanalysisofinhospitalmortalityinemergencyandcriticalcare
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Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care

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