Stanford researchers have developed a method for assessing neonatal health risk by using longitudinal electronic health records (EHR) utilizing a machine learning model comprising deep learning neural networks.
Stanford inventors have developed TrueImage, a machine learning algorithm to assess the quality of patient images sent in for telemedicine appointments.
Stanford researchers have developed an algorithm using deep learning architectures to predict cardiac function (ejection fraction) and trace the endocardium of the left ventricle from ultrasound echocardiogram videos.
This software tool takes clinical notes from veterinary electronic medical records and assigns SNOMED-CT VET extension diagnostic codes based on the content written on the notes.