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With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. To address this challenge we designed, developed, and tested a predictive healthcare data analytics and communication framework called RASPRO (Rapid Active Summarization for effective PROgnosis) in a collaborative work with doctors.
In RASPRO we built a novel three-step technique to derive high performance alerts from voluminous sensor data (as illustrated in Fig 1).
The RASPRO framework was validated in clinical deployments across multiple specialities in our super speciality hospital.
The suitability of RASPRO framework in global health deployment demonstrates its advantages, namely:
Funding agency: SERB, Govt. of India
Project Title: Development, optimization and pilot evaluation of edge-AI enabled photoplethysmograph IoT devices and clinical data summarization methods for remote patient monitoring and disease detection
Grant No.: SRG/2020/001119
Dr. Prakash Ishwar, Professor (ECE, SE), Boston University
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