Predictive Modelling of Critical Vital Signs in ICU Patients by Machine Learning: An Early Warning System for Improved Patient Outcomes

Abstract

Accurate monitoring of vital signs in an ICU is integral to understanding overall physical well-being for patients. Our research endeavor employed machine learning techniques to construct a predictive classification model utilizing continuous ICU vital sign measurements. The primary aim was to develop an early warning system capable of forecasting whether vital indicators would reach critical values within one hour; our ultimate aim was to enable healthcare professionals, including nurses and doctors, to intervene proactively, preventing emergency situations which could result in organ dysfunction or mortality. Our comprehensive dataset comprises vital sign measurements, lab test results, procedures, and medications from over 50,000 patients collected via rigorous preprocessing procedures like data cleansing, bias correction, feature extraction and selection to produce an insightful dataset with distinguishing attributes. After selecting an algorithmic set that included Decision Trees (DT), Support Vector Machines (SVM), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), to predict critical vital signs in ICU patients one hour in advance - such as Heart Rate, SpO2, Mean Artery Pressure (MAP), Respiratory Rate (RR), and Systolic Blood Pressure (SBP). Our models included Heart Rate prediction as well as respiratory Rate/RR predictions/SBP estimation models. The results of the study demonstrated the efficacy and accuracy of machine learning methods designed to anticipate imminent changes to vital signs. Utilizing such predictive models, healthcare providers can increase their capacity to address potential complications before they occur, ultimately leading to improved patient outcomes in challenging settings.

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