Big data analytics for continuous assessment of astronaut health risk and its application to human-in-the-loop (HITL) related aerospace

© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The man-instrumentation-equipment-vehicle-environment ecosystem is complex in aerospace missions. Health status of the individual has important implications on decision making and performance that should be factored into assessments for probability of success/risk of failure both in offline and real-time models. To date probabilistic models have not considered the dynamic nature of health status. Big Data analytics is enabling new forms of analytics to assess health status in real-time. There is great potential to integrate dynamic health status information with platforms assessing risk and the probability of success for dynamic individualized real-time probabilistic predictive risk assessment. In this research we present an approach utilizing Big Data analytics to enable continuous assessment of astronaut health risk and show its implications for integration with HITL related aerospace mission

Towards of a real-time Big Data architecture to intensive care

These days the exponential increase in the volume and variety of data stored by companies and organizations of various sectors of activity, has required to organizations the search for new solutions to improve their services and/or products, taking advantage of technological evolution. As a response to the inability of organizations to process large quantities and varieties of data, in the technological market, arise the Big Data. This emerging concept defined mainly by the volume, velocity and variety has evolved greatly in part by its ability to generate value for organizations in decision making. Currently, the health care sector is one of the five sectors of activity where the potential of Big Data growth most stands out. However, the way to go is still long and in fact there are few organizations, related to health care, that are taking advantage of the true potential of Big Data. The main target of this research is to produce a real-time Big Data architecture to the INTCare system, of the Centro Hospitalar do Porto, using the main open source big data solution, the Apache Hadoop. As a result of the first phase of this research we obtained a generic architecture who can be adopted by other Intensive Care Units."This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013." This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-00002

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

STDMn+p0: a multidimensional patient oriented data mining framework for critical care research

In the neonatal intensive care unit (NICU) environment, critical care and treatment directly correlate to the multidimensional development of an infant and are influenced by attributes such as gender and gestational age (GA). Recent literature on guidelines developed for neonatal intensive care; do not take the gender or the GA of the infant into account. The exponential activity of a growing neonate in its early stages of life needs to be captured and embedded into algorithms designed to extract patterns of predictive temperament within the NICU domain. The STDMn+p0 framework presents an extended multidimensional approach with the ability to create patient characteristic clinical rules. Further defining NICU algorithms, through the extended use of attributes to include gender and GA, and using these new algorithms in clinical decision support systems increases the accuracy and thereby minimizes the risk of adverse events

Adaptive API for Real-Time Streaming Analytics as a Service.

A significant amount of physiological data is generated from bedside monitors and sensors in neonatal intensive units (NICU) every second, however facilitating the ingestion of such data into multiple analytical processes in a real time streaming architecture remains a central challenge for systems that seek effective scaling of real-time data streams. In this paper we demonstrate an adaptive streaming application program interface (API) that provides real time streams of data for consumption by multiple analytics services enabling real-time exploration and knowledge discovery from live data streams. We have designed, developed and evaluated an adaptive API with multiple ingestion of data streamed out of bedside monitors that is passed to a middleware for standardization and structuring and finally distributed as a service for multiple analytical services to consume and perform further processing. This approach allows, (a) multiple applications to process the same data streams using multiple algorithms, (b) easy scalability to manage diverse data streams, (c) processing of analytics for each patient monitored at the NICU, (d) ability to integrate analytics that seek to evaluate multiple patients at the same point in time, and (e) a robust automated process with no manual interruptions that effectively adapts to changing data volumes when bedside monitors increases or the amount of data emitted by a monitor changes. The proposed architecture has been instantiated within the Artemis Platform which provides a framework for real-time high speed physiological data collection from multiple and diverse bed side monitors and sensors in NICUs from multiple hospitals. Results indicate this is a robust approach that can scale effectively as data volumes increase or data sources change

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

A flexible, longitudinal and surrogate consent model: Consent of Infants for Neonatal Secondary-use research (CoINS) Model

Documenting healthcare, along with technology enabling capture of streaming patient telemetry, can deliver large datasets offering opportunities to discover new insights primarily identified through retrospective secondary use research. Research involving health data requires consent of the subject patient or someone with the power to speak on that patient???s behalf. Flexible consent models that capture consent preferences while allowing updates as preferences change are needed. This research proposes and demonstrates one solution in a case study collecting surrogate consent from parents for the physiological data of infant inpatients in the Neonatal Intensive Care Unit (NICU) and attaching this consent as a wrapper controlling access to their data. 145 parents were approached and 134 provided consent: with 78 percent of infants consented during their first week of life. This research supports the contention that using a flexible consent approach enhances willingness to consent use of infant???s health data for secondary research purposes