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Hospital Readmission Risk

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Hospital Readmission Risk

Problem/Challenge – Patients are discharged after they are treated with serious and chronic illnesses in the hospital. Once these patients leave the hospital, it is likely that up to 25% of these patients will be readmitted within 30 days to be treated again. Many of these readmissions are believed to be preventable, clinicians often lack the appropriate tools to identify which patients are most likely to be readmitted.

Resolution – Machine learning algorithms can be trained for each hospital and weighted for individual characteristics such as patient’s recent care, their current condition, treatment, length of stay, their home life and other risk factors. An AI based risk assessment tool can help prevent readmission, saving costs and improving quality of treatment.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting patients who are at risk before they are discharged, it will automatically engineer and identify the individual reasons for why each patient will be readmitted. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.