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

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

A dynamic visual analytics framework for complex temporal environments

Introduction: Data streams are produced by sensors that sample an external system at a periodic interval. As the cost of developing sensors continues to fall, an increasing number of data stream acquisition systems have been deployed to take advantage of the volume and velocity of data streams. An overabundance of information in complex environments have been attributed to information overload, a state of exposure to overwhelming and excessive information. The use of visual analytics provides leverage over potential information overload challenges. Apart from automated online analysis, interactive visual tools provide significant leverage for human-driven trend analysis and pattern recognition. To facilitate analysis and knowledge discovery in the space of multidimensional big data, research is warranted for an online visual analytic framework that supports human-driven exploration and consumption of complex data streams. Method: A novel framework was developed called the temporal Tri-event parameter based Dynamic Visual Analytics (TDVA). The TDVA framework was instantiated in two case studies, namely, a case study involving a hypothesis generation scenario, and a second case study involving a cohort-based hypothesis testing scenario. Two evaluations were conducted for each case study involving expert participants. This framework is demonstrated in a neonatal intensive care unit case study. The hypothesis generation phase of the pipeline is conducted through a multidimensional and in-depth one subject study using PhysioEx, a novel visual analytic tool for physiologic data stream analysis. The cohort-based hypothesis testing component of the analytic pipeline is validated through CoRAD, a visual analytic tool for performing case-controlled studies. Results: The results of both evaluations show improved task performance, and subjective satisfaction with the use of PhysioEx and CoRAD. Results from the evaluation of PhysioEx reveals insight about current limitations for supporting single subject studies in complex environments, and areas for future research in that space. Results from CoRAD also support the need for additional research to explore complex multi-dimensional patterns across multiple observations. From an information systems approach, the efficacy and feasibility of the TDVA framework is demonstrated by the instantiation and evaluation of PhysioEx and CoRAD. Conclusion: This research, introduces the TDVA framework and provides results to validate the deployment of online dynamic visual analytics in complex environments. The TDVA framework was instantiated in two case studies derived from an environment where dynamic and complex data streams were available. The first instantiation enabled the end-user to rapidly extract information from complex data streams to conduct in-depth analysis. The second allowed the end-user to test emerging patterns across multiple observations. To both ends, this thesis provides knowledge that can be used to improve the visual analytic pipeline in dynamic and complex environments