PRINCETON, N.J. – Engineers from Princeton University are working to use sensor wearable sensor technology to develop software that could diagnose multiple diseases in real-time, like warning a patient who is developing diabetes.
In a recently published paper, “IEEE Transactions on Multi-Scale Computing Systems,” researchers led by Niraj Jha wrote that their system, the Hierarchical Health Decision Support System, used biomedical data to successfully detect five diseases in simulations created from patient data. The system diagnosed Type 2 diabetes with 78% accuracy, arrhythmia with 86% accuracy, urinary bladder disorder with 99% accuracy, hypothyroid with 95% accuracy and renal pelvis nephritis with 94% accuracy.
“This opens up the possibility for the first time that outside of a clinic, individuals can monitor whether they have developed or can develop a disease,” said Jha, a professor of electrical engineering, who developed the new technology with Hongxu Yin, an electrical engineering Ph.D. student.
HDSS used publicly available, anonymized biomedical data from hundreds of patients and fed it through eight machine-learning algorithms that had been trained by the researchers to recognize typical signs of these diseases. The data consisted of physiological measurements collected by commercially available medical sensors that are embedded in small electronic devices attached to hospital patients to track things like blood pressure and galvanic skin response.
The HDSS system is unique because it compares the data points to publicly available data about disease symptoms. This allows the software to detect signs of trouble that patients aren’t aware of, or symptoms that they fail to reveal to their doctors.
Jha wrote that rather than focus on in-patient treatment, the team is working to apply data from wearable sensors intended for everyday use, such as watches or wristbands. The approach would provide physicians with symptomatic information that patients might have forgotten or not noticed, and would also allow for monitoring patients after a diagnosis.
The researchers said the ultimate goal is both to increase efficiency in health care, and to allow for earlier diagnoses and better patient outcomes. Yin said the researchers eventually would like to expand the type of data available for use in diagnoses, such as patient records or genetic information.