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Predicting Sepsis Risk

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Predicting Sepsis Risk

Problem/Challenge – Sepsis is a life-threatening condition that can occur in surgery patients. Around 80% of sepsis deaths could be prevented with rapid diagnosis and treatment according to Johns Hopkins Armstrong Institute for Patient Safety and Quality. Detecting sepsis can be really difficult because its signs and symptoms can be caused by other disorders.

Resolution – Machine learning algorithms can be leveraged to detect patterns in the data in all patients at the same time to detect risk of sepsis. An early diagnosis using machine learning algorithms can be done on routine vital signs and metabolic levels from electronic medical records can highlight patients at risk for Sepsis before they are admitted to the ICU. An AI based risk assessment tool can help prevent aggressive and more costly treatments which would therein improve patient outcomes.

Why CafeEDA – CafeEDA’s aim is to leverage AI in accurately predicting patients who are at risk of getting sepsis before their condition gets matured, it will automatically engineer and identify the individual reasons for why each patient will be at a risk. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeEDA 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.