Quality healthcare transcends the medical profession, as evidenced by a new project led by West Virginia University that includes not only health experts but engineers, a physicist, a lawyer and a business data analyst.
“Bridges in Digital Health,” which recently received $3 million from the National Science Foundation, hopes to address the combination of rising healthcare costs, the expansion of the nation’s elderly population and health disparities, particularly in rural communities, through advances in digital health and artificial intelligence, and training the next generation of professionals to develop and deploy such advances.
Digital health is a rapidly growing field that involves clinical and biomedical data including prescriptions, medical images, ultrasound videos, electronic health records and data from mobile devices and wearables, such as Fitbit, said Donald Adjeroh, lead investigator of the project and professor and associate chair in the Lane Department of Computer Science and Electrical Engineering.
Two of our pathway themes in the project are focused on the use of data science and A.I. on two key areas in healthcare: namely, cardiovascular health (analysis of cardiac images, especially, echocardiograms), and genomics (analysis and functional annotation of long non-coding ribonucleic acids – a type of RNA – and their role in disease prediction and prognosis).”
Donald Adjeroh, lead investigator of the project
“Apart from traditional electronic health records, our health data will come from different sources and devices, including wearable devices such as hand-held mobile cardiac ultrasound devices, or pocket EKG monitors, low-cost mobile activity monitors, Fitbits, smart watches, social media, etc. Such low-cost wearable devices and data sources are important in collecting health-related data from individuals in rural areas, and outside the hospital setting, important for preventive care.”
Adjeroh noted that various recent reports, including results from WVU labs, document the success stories of A.I. techniques on health problems including breast cancer detection, diagnosing eye diseases, reading cardiac ultrasound images, early prediction of acute kidney failure, predicting adverse drug events and visualization of neuronal structures in the brain.
“These methods have shown performance that are close to human performance, and at times outperform human professionals on some of these tasks,” he said.
The NSF funding will help establish a new graduate education and traineeship model to prepare students to work in collaborative teams to develop and apply data science and A.I. techniques in addressing digital health issues. The project anticipates training 24 funded and 40 unfunded master’s and doctoral students from different disciplines including engineering, computer science, medicine, health sciences, physical sciences and economics.