The prediction of mortality influential variables in an intensive care unit: a case study
- 26 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Personal and Ubiquitous Computing
- Vol. 27 (2), 203-219
- https://doi.org/10.1007/s00779-021-01540-5
Abstract
The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients’ demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.Keywords
This publication has 61 references indexed in Scilit:
- 2013 ESH/ESC Guidelines for the management of arterial hypertensionEuropean Heart Journal, 2013
- Deep Vein Thrombosis Prophylaxis in Trauma PatientsThrombosis, 2011
- Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*Critical Care Medicine, 2011
- Inability of Providers to Predict Unplanned ReadmissionsJournal of General Internal Medicine, 2011
- A decision tree method for building energy demand modelingEnergy and Buildings, 2010
- Deaths Preventable in the U.S. by Improvements in Use of Clinical Preventive ServicesAmerican Journal of Preventive Medicine, 2010
- Hyperglycemic Crises in Adult Patients With DiabetesDiabetes Care, 2009
- The random subspace method for constructing decision forestsIeee Transactions On Pattern Analysis and Machine Intelligence, 1998
- A case-control study of patients readmitted to the intensive care unitCritical Care Medicine, 1993
- Patient readmission to critical care units during the same hospitalization at a community teaching hospitalIntensive Care Medicine, 1983