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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: Haas, Oliver, Maier, Andreas, Rothgang, Eva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606583/
https://www.ncbi.nlm.nih.gov/pubmed/34820408
http://dx.doi.org/10.3389/fmed.2021.785711
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author Haas, Oliver
Maier, Andreas
Rothgang, Eva
author_facet Haas, Oliver
Maier, Andreas
Rothgang, Eva
author_sort Haas, Oliver
collection PubMed
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.
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spelling pubmed-86065832021-11-23 Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care Haas, Oliver Maier, Andreas Rothgang, Eva Front Med (Lausanne) Medicine 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. Frontiers Media S.A. 2021-11-08 /pmc/articles/PMC8606583/ /pubmed/34820408 http://dx.doi.org/10.3389/fmed.2021.785711 Text en Copyright © 2021 Haas, Maier and Rothgang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Haas, Oliver
Maier, Andreas
Rothgang, Eva
Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care
title 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_short 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
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606583/
https://www.ncbi.nlm.nih.gov/pubmed/34820408
http://dx.doi.org/10.3389/fmed.2021.785711
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