High frequency distributed data stream event correlation to improve neonatal clinical management

Approximately eighteen percent (18%) of babies born in New South Wales (NSW), Australia require special care or neonatal intensive care admission. Premature babies can be up to 17 weeks early and may only weigh 450gms; they can spend 3 or 4 months in intensive care and have dozens of specific diseases before discharge, many of these may have long term implications for the future health of the individual. In addition, fifteen percent of neonatal intensive care admissions are transferred after delivery from smaller regional or remote hospitals without intensive care facilities to larger Tertiary Referral or Children's Hospitals with Neonatal Intensive Care Units (NICUs). Similar conditions apply within Australia, New Zealand, Canada, USA and elsewhere where small non-tertiary units are spread throughout the country. This paper presents case study based applied research in progress supporting the development of a distributed event stream processing framework to enable high frequency distributed data stream event correlation to improve neonatal clinical management. This research extends the traditional notion of event-based approaches by extending the notion of an event to incorporate a composite event that exists over a period of time, as is required within the domain of health and medicine. This is achieved through a multi-agent event calculus based approach that supports temporal abstraction. A key contribution of this research is the ability to support automated medical condition onset detection

SDTDMn0 : a multidimensional distributed data mining framework supporting time series data analysis for critical care research

Premature birth is one of the major perinatal health issues across the world. In 2007, the estimated Canadian preterm birth rate was 8.1 % (CIHI, 2009). Recent research has shown that conditions, such as nosocomial infections or apnoeas, exhibit certain variations in the baby's physiological parameters which can indicate the onset of the event before it can be detected by physicians and nurses. Neonatal Intensive Care Units are some of the highest information producing areas in hospitals. The multidimensional and distributed nature of the data further adds another layer of complexity as physiological changes can occur in one data stream or can be cross-correlated between several streams. With the collection and storage of electronic data becoming a global trend, there is an opportunity to analyse the collected data in order to extract meaningful information and improve healthcare. The aforementioned properties of the data motivate the need for a framework that supports analysis and trend detection in a multidimensional and distributed environment

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions