Allocation of Medical Resources with Supervised Learning

In today’s medical environment, the allocation problem caused by the shortage of medical resources has an increasingly prominent impact on the efficiency of medical services. With the increasing application value of artificial intelligence methods such as machine learning, this paper will use this to explore how to optimize the allocation of medical resources. Given that electronic medical record data contains rich patient information, this study innovatively introduced a random forest supervised learning model based on the electronic medical record data of a large number of cardiovascular patients to accurately predict the auxiliary examinations required by patients and the expected number of days in hospital. Through in-depth mining and analysis of multi-dimensional data in electronic medical records, the advantages of the random forest model in processing multidimensional data characteristics are fully utilized to train and optimize the model. It aims to provide medical institutions with scientific and accurate decision-making basis in the clinical field, so as to better plan and allocate medical resources, improve the utilization efficiency of medical resources and the quality of medical services. The experimental results show that the random forest model based on electronic medical record data has significant accuracy and reliability in predicting auxiliary examinations and hospital days, providing a feasible method with broad application prospects for solving the planning and allocation problems of medical resources in actual environments.