Diagnosis Accuracy for Patients
An early-stage health tech company was using IoT sensors to collect data from patients with implanted cardiac pacemakers. The data was then transmitted to the cloud and used to diagnose health conditions such as hypertension, heart arrhythmia, atrial fibrillation, heart attacks, etc. However, the company’s existing models only delivered 50% accuracy in the analysis and classification of the data. By partnering with NorthBay to develop a new data lake that enabled them to more broadly experiment with ML and model training, they increased the accuracy of their IoT data to 95%.