From Queue to Score: Enhancing Effective Organ Allocation using Machine Learning

Transplantation of organs is a drastic measure in the treatment of persons with organ dysfunction on the terminal stage of organ failure. Nonetheless, the existing organ allocation system that depends on a seniority basis on the waiting list is not efficient enough to respond to the degree of the patients' urgency since it disregards the patient's medical status resulting in poor performances, and avoidable deaths among the patients. The research paper presents a new approach to the allocation of organs that focuses on the machine learning of the patients' severity scores which are based on patients' vital signs. Research Methodology is explorative in nature Since the approach builds upon an always available dynamic database that updated with data on what it is as regards to the patient's lung and heart conditions, blood pressure, and oxygen levels. This information is then examined by applying multiple regression analysis as a form of machine learning to derive a comprehensive severity score that indicates the level of emergency in each patient's case. Concerning the key problem of patients' deaths while waiting for transplants, the proposed system intends to improve the relationship between the degree of organs' allocation and medical necessity. The use of real-time data and a sophisticated set of AI techniques increases the equity and effectiveness of the distribution process, making it possible to deliver organs to patients requiring them most. This paper also compares patient databases in other countries including India, the probabilistic use of machine learning to arrive at severity scores and the ethical parameters of the application of AI based systems.