We solve the problem that every hardware company faces at the some point in their sales cycle. To create an accurate predictive model, sensors need to combine medical records with vital signs. Then they need to create a machine learning model and train it to produce results. Companies succeed at the first step of obtaining vitals but they get stuck when they need to create an accurate predictive algorithm using medical data. #1 reason for that is access to personally identifiable data. Healthcare organizations don't let startups toy around with their patients' data -- rightfully so bacause training a model that contains PII in the cloud is not secure. Friendly solves this problem by removing and masking PII data, creating a machine learning ready dataset, and training it in the cloud. After training is done, the model is downloaded on local servers and PII data is re-stitched. Pilots with major life science and medical billing organizations have shown a lot of promise for Friendly technology. We identified and quantified visceral fat as a predictor of diabetes. analyzed ultrasound images to automate some of pregnancy followups, and analyzed physician notes to determine under-billing opportunities.
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