A cloud based big data health-analytics-as-a-service framework to support low resource setting neonatal intensive care unit

© 2020 ACM. Critical care patients are monitored by a range of medical devices collecting high frequency data. New computing frameworks and platforms are being proposed to review and analyze the data in detail. The application of these approaches in a low resource setting is challenged by the approaches used for data acquisition. Software as a Service (SaaS) is a form of cloud computing where a cloud-based software application enables the storage, analysis and visualization of data within the cloud. A subset of SaaS is Health Analytics as a Service (HAaaS), which provides software to support health analytics in the cloud. The objective of this study is to design, implement, and demonstrate an extendable big-data compatible HAaaS framework that offers both real-time and retrospective analysis where data acquisition is not tightly coupled. A data warehousing framework is presented to facilitate analysis within a low resource setting. The framework has been instantiated in the Artemis platform within the context of the Belgaum Children Hospital (BCH) case study. Initial end-to-end testing with the Nellcor monitor (bedside monitor at BCH), which was not connected to any human, was completed. This testing confirms the functionality of the new Artemis cloud instance to receive data from test device using an alternate data acquisition approach

Correlation and real time classification of physiological streams for critical care monitoring.

This thesis presents a framework for the deployment of algorithms that support the correlation and real-time classification of physiological data streams through the development of clinically meaningful alerts using a blend of expert knowledge in the domain and pattern recognition programming based on clinical rules. Its relevance is demonstrated via a real world case study within the context of neonatal intensive care to provide real-time classification of neonatal spells. Events are first detected in individual streams independently; then synced together based on timestamps; and finally assessed to determine the start and end of a multi-signal episode. The episode is then processed through a classifier based on clinical rules to determine a classification. The output of the algorithms has been shown, in a single use case study with 24 hours of patient data, to detect clinically significant relative changes in heart rate, blood oxygen saturation levels and pauses in breathing in the respiratory impedance signal. The accuracy of the algorithm for detecting these is 97.8%, 98.3% and 98.9% respectively. The accuracy for correlating the streams and determining spells classifications is 98.9%. Future research will focus on the clinical validation of these algorithms and the application of the framework for the detection and classification of signals in other clinical contexts