The prediction of mortality influential variables in an intensive care unit: a case study

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.

Preditores de mortalidade e tempo médio de sobrevivência dos pacientes críticos

“…Logo, é necessária a utilização de escores clínicos capazes de predizer o risco de morte desta população, a exemplo do SOFA e do SAPS 3. (16,17) Outro achado importante do presente estudo foi a correlação entre os maiores escores do SAPS-3 e NAS com o tempo de internação prolongado. A procedência da emergência também contribuiu para o aumento do SAPS 3.…”

Heart disease risk prediction using deep learning techniques with feature augmentation

“…For these reasons, many authors have developed methods that help in the detection of heart disease, by taking into account different factors. Most of this methods use machine learning techniques to prevent the problems derived from statistical analysis methods, that fail to capture prognostic information in large datasets containing multi-dimensional interactions [3,17,18,25,26]. Some of these papers have generally benefited from large datasets that allow detection of existing diseases thanks to historical data over a long period of time.…”

A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods

“…The advent of AI has led to significant developments in numerous modern applications, such as air quality, weather monitoring, and video surveillance [ 1 , 2 ]. Nowadays, ML algorithms and intelligent applications have made it possible to analyze various types of data, including text, numeric, photographs, videos, and locations, from different IoT devices [ 20 , 43 , 44 ]. However, ML typically employs centralized data, which raises several problems [ 45 ].…”