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USE CASE

Predicting Flu Encounters

Objective

Companies are leveraging AI, and machine learning to create infectious disease forecasting. For flu predictions, researchers use data sets, Google searches, doctor's visits and academic research. ML can be leveraged to find hidden patterns within masses of disease indicator data.

Outcome

The application of machine learning and the use of artificial intelligence is currently changing the way the spread of the diseases is addressed by modelling the course of the virus and finding new strategies for containment. The information can be used for predictive models that forecast when and where flu activity increases in states and cities around the country.

Business Value

Forecasting saves lives, as public measures can be taken to keep people safe. The CDC estimates that between 12,000 and 61,000 people die each year from the flu.

H2O's AI and Data Approaches

This solution  is powered by the H2O AI Cloud, Driverless AI, AutoML, and H2O.ai Wave. The data science approaches include regression, classification, time series, stacked ensembles, SEIRD, power growth, and advanced feature engineering.

 

Resources

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