Churn Prediction of Clinical Decision Support Recommender System

The clinical decision support systems (CDSS) are advanced technologies intended to facilitate caregivers in making diagnostic decisions regarding individual patients. These systems are efficient intelligence technologies that produce particular instance recommendations based on multiple parts of health records. We propose a churn prediction clinical decision support system based on the proper recommendation system for data-determined treatment decision support. Hospitals and chronic disease hospitals can use structured data and prescriptions from electronic health records to predict which patients will likely receive care from their facilities. For implementation purposes, using ensemble learning techniques for predicting patient churn and clustering techniques based on diagnosis urgency is of interest to clinical decision-makers. Such information can help in diagnosis.