Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling
dc.creator | Nakas C.T., Schütz N., Werners M., Leichtle A.B.L. | en |
dc.date.accessioned | 2023-01-31T09:03:06Z | |
dc.date.available | 2023-01-31T09:03:06Z | |
dc.date.issued | 2016 | |
dc.identifier | 10.1371/journal.pone.0159046 | |
dc.identifier.issn | 19326203 | |
dc.identifier.uri | http://hdl.handle.net/11615/76881 | |
dc.description.abstract | Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures. © 2016 Nakas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en |
dc.language.iso | en | en |
dc.source | PLoS ONE | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978720926&doi=10.1371%2fjournal.pone.0159046&partnerID=40&md5=a45e7e8da5ba37ef646b7bcdccf6c025 | |
dc.subject | artificial neural network | en |
dc.subject | calculation | en |
dc.subject | calibration | en |
dc.subject | data base | en |
dc.subject | decision support system | en |
dc.subject | decision tree | en |
dc.subject | electronic health record | en |
dc.subject | hospital admission | en |
dc.subject | hospital mortality | en |
dc.subject | human | en |
dc.subject | intermethod comparison | en |
dc.subject | laboratory test | en |
dc.subject | learning | en |
dc.subject | logistic regression analysis | en |
dc.subject | mortality risk | en |
dc.subject | nervous system | en |
dc.subject | receiver operating characteristic | en |
dc.subject | statistical model | en |
dc.subject | university hospital | en |
dc.subject | age | en |
dc.subject | calibration | en |
dc.subject | decision support system | en |
dc.subject | electronic health record | en |
dc.subject | factual database | en |
dc.subject | female | en |
dc.subject | hospital patient | en |
dc.subject | male | en |
dc.subject | procedures | en |
dc.subject | risk assessment | en |
dc.subject | sex difference | en |
dc.subject | statistical model | en |
dc.subject | Age Factors | en |
dc.subject | Calibration | en |
dc.subject | Databases, Factual | en |
dc.subject | Decision Support Techniques | en |
dc.subject | Electronic Health Records | en |
dc.subject | Female | en |
dc.subject | Hospital Mortality | en |
dc.subject | Humans | en |
dc.subject | Inpatients | en |
dc.subject | Male | en |
dc.subject | Models, Statistical | en |
dc.subject | Risk Assessment | en |
dc.subject | Sex Factors | en |
dc.subject | Public Library of Science | en |
dc.title | Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling | en |
dc.type | journalArticle | en |
Fichier(s) constituant ce document
Fichiers | Taille | Format | Vue |
---|---|---|---|
Il n'y a pas de fichiers associés à ce document. |