Multifaceted Feature Extraction and Machine Learning Approaches for Mortality Risk Prediction in Pediatric Intensive Care Units

In an Intensive Care Unit scenario, estimating the risk of a patient dying is useful for improving patient care and efficiently utilizing limited resources. Traditional scoring systems are available, but modern and promising methods from data-driven techniques have been shown to improve mortality prediction, specifically to Pediatric Intensive Care Units(PICUs). This research focuses on developing and implementing machine learning prediction techniques for mortality risk that uses a large dataset extracted from a single hospital database comprised of multiple tables using rigorous data preprocessing and cleaning work. The multiple tables in the large hospital database provide a collection of patient data that this research effectively organizes and integrates. The report includes patient demographics, admission details, and ICU stay details. Patient demographics, admission specifics, and information about ICU stays are among the elements of the hospital database that aid in the synthesis of a patient profile. This profile combines biometric information, admission details, measures associated with ICU stays, and whether the patient had an outcome of survival or survival outcome. In this paper, an approach is demonstrated to work with a huge single hospital patient dataset to merge classes and perform the feature extraction process with the use of various machine learning models for mortality risk prediction of the patients. This work contributes to the field of medicine, where disparate data is integrated, and advanced machine learning algorithms are used to improve mortality risk estimates and clinical decisions.