This Machine Learning script within Interplay® takes in basic health information such as age, sex, blood pressure, serum cholesterol, max heart rate, and other lab-measured inputs to return a predicted risk of suffering a heart attack. This is not intended as an in-clinic official procedure yet, but it does show the increasing capabilities to draw reasonable common-sense inferences from common data points.
Such predictor models could be helpful in personal health apps as consumers look to monitor their own health at a more granular level. Models like this could also be impactful in the macro-level, where public policy can be informed by basic demographic information and appropriate medical care costs can be estimated (e.g. a retirement community would have different models compared to a young suburban community).
The core AI predictor within Interplay could be easily connected to messaging apps via twilio, chatbots, or other messaging platforms. It could also be incorporated back into a legacy code base for a hospital group or public policy database.
