ICU Patients’ Pattern Recognition and Correlation Identification of Vital Parameters Using Optimized Machine Learning Models

Early detection of patient deterioration in the Intensive Care Unit (ICU) can play a crucial role in improving patient outcomes. Conventional severity scales currently used to predict patient deterioration are based on a number of factors, the majority of which consist of multiple investigations. Recent advancements in machine learning (ML) within the healthcare domain offer the potential to alleviate the burden of continuous patient monitoring. In this study, we propose an optimized ML model designed to leverage variations in vital signs observed during the final 24 hours of an ICU stay for outcome predictions. Further, we elucidate the relative contributions of distinct vital parameters to these outcomes The dataset compiled in real-time encompasses six pivotal vital parameters: systolic (0) and diastolic (1) blood pressure, pulse rate (2), respiratory rate (3), oxygen saturation (SpO2) (4), and temperature (5). Of these vital parameters, systolic blood pressure emerges as the most significant predictor associated with mortality prediction. Using a fivefold cross-validation method, several ML classifiers are used to categorize the last 24 hours of time series data after ICU admission into three groups: recovery, death, and intubation. Notably, the optimized Gradient Boosting classifier exhibited the highest performance in detecting mortality, achieving an area under the receiver-operator curve (AUC) of 0.95. Through the integration of electronic health records with this ML software, there is the promise of early notifications regarding adverse outcomes, potentially several hours before the onset of hemodynamic instability

A Framework for Web Services Retrieval Using Bio Inspired Clustering

Efficiently discovering relevant Web services with respect to a specific user query has become a growing challenge owing to the incredible growth in the field of web technologies. In previous works, different clustering models have been used to address these issues. But, most of the traditional clustering techniques are computationally intensive and fail to address all the problems involved. Also, the current standards fail to incorporate the semantic relatedness of Web services during clustering and retrieval resulting in decreased performance. In this paper, we propose a framework for web services retrieval that uses a bottom-up, decentralized and self organising approach to cluster available services. It also provides online, dynamic computation of clusters thus overcoming the drawbacks of traditional clustering methods. We also use the semantic similarity between Web services for the clustering process to enhance the precision and lower the recall