Asian Journal of Medical Research and Case Reports

In order to greatly improve patient outcomes and reduce mortality rates, the study highlights how important it is to identify potential deterioration in intensive care units (ICUs) early on. Sadly, the complex patterns in physiological data over time are frequently difficult for conventional clinical scoring systems to identify. The study concentrated on developing and evaluating time series models that use ongoing vital sign monitoring to predict patient decline in order to address this problem. Data from the Medical Information Mart for Intensive Care III (MIMIC-III) database, which included 5,847 intensive care unit patients, was used in the analysis using R version 4.3.0. Auto Regressive Integrated Moving Average (ARIMA), Random Forest classifiers, and Long Short-Term Memory (LSTM) neural networks were the three predictive models that were used and contrasted. Vital signs such as temperature, oxygen saturation, respiratory rate, heart rate, and blood pressure were among the input features. A number of outcomes, including cardiac arrest, an unexpected ICU readmission, or death within 24 hours, were used to define patient deterioration. The results showed that the LSTM model performed better than the others, with an Area under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.87 compared to 0.75 for Random Forest and 0.68 for ARIMA. The efficacy of the LSTM model in identifying actual deterioration events was further demonstrated by its remarkable accuracy (83%), precision (79%), recall (85%), and F1-score of 82%. According to the study's findings, time series deep learning models—particularly LSTMs—have a lot of promise for identifying patient decline early in intensive care unit settings to improve patient safety, recuperation, reduction in preventable disease, enhance the effective use of available resources etc. Implementing these models could lead to timely clinical interventions, ultimately enhancing patient outcomes.