Machine Learning Approaches for Enhancing Health Outcomes in Pediatrics: AI Models for Personalized Treatment, Monitoring, and Early Intervention in Children

Abstract

The application of machine learning (ML) in pediatrics is a developing field. ML teaches computers how to carry out tasks by learning from data rather than being explicitly programmed. This is especially useful when the task is complex and subtle, and there is a large amount of data that directs an optimal way of handling the task. This is particularly relevant in pediatrics, where each child is different, aiding in advancing the field to cater personalized treatment, monitoring, and early intervention. Healthcare globally is rapidly undergoing modernization, leveraging advanced computational techniques. One widely researched and applied field is the use of computational algorithms to assist clinical decision-making, i.e., precision medicine-based clinical decision support systems. In the pediatric setting, ML applications could potentially impact and improve a broad spectrum of clinical scenarios that could benefit children's health, such as rapid treatment decisions for pediatric trauma scenarios, proactive individual patient monitoring, personalizing antibiotic doses or food intake requirements, learning to time ICC insertion in pancreatitis, and learning from pediatric trauma interventions remotely to provide training in low- to middle-income countries. The variety and application potential range widely, including common clinical scenarios that are seen every day across the world to high-priority research areas where immediate therapy is needed for rare patients. ML is a coding method for training computers to recognize patterns in data. Machine learning has transformed the world of healthcare delivery. There are a number of machine learning techniques used, including the deployment of large databases tracking patient care over time, appointment patterns, and events. Belief networks and natural language processing allow computers to process dialogue and find information in large textual documents to be mined. Similar patient matching: the computer finds patients that are similar to a patient I am treating to see if I can find a trajectory for disease using multidimensional data. Prediction of various outcomes based on a new patient's characteristics. Prediction systems with machine learning determine syndromes over a probability space. Predicting health outcomes such as trauma outcomes using machine learning includes pediatric emergency patients. Modeling complex biological responses where expert systems assess the integration of a patient's clinical and molecular data to match them to appropriate clinical trials.