Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Pandemics: Insurance and Social Protection

This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing in the presence of low probability events, group testing, compositional data analysis for detecting data inconsistencies, behaviouristic aspects in fighting a pandemic, and insurers’ legal problems, amongst others. Concluding with an essay by a practicing actuary on the applicability of the methods proposed, this interdisciplinary book is aimed at actuaries as well as readers with a background in mathematics, economics, statistics, finance, epidemiology, or sociology

Modelling complex systems in the context of the COVID-19 pandemics

Systems biology is an interdisciplinary approach investigating complex biological systems at different levels by combining experimental and modelling approaches to understand underlying mechanisms of health and disease. Complex systems including biological systems are affected by a plethora of interactions and dynamic processes often with the aim to ensure robustness to emer- gent system properties. The need for interdisciplinary approaches became very evident in the recent COVID-19 pandemic spreading around the globe since the end of 2019. This pandemic came with a bundle of urgent epidemiological open questions including the infection and transmis- sion mechanisms of the virus, its pathogenicity and the relation to clinical symptoms. During the pandemic, mathematical modelling became an essential tool to integrate biological and healthcare data into mechanistic frameworks for projections of future developments and the assessment of different mitigation strategies. In this regard, systems biology with its interdisciplinary approach was a widely applied framework to support society in the COVID-19 crisis. In my thesis, I applied different mathematical modelling approaches as a tool to identify underlying mechanisms of the complex dynamics of the COVID-19 pandemic with a specific focus on the situation in Luxembourg. For this purpose, I analysed the COVID-19 pandemic at its different phases and from various perspectives by investigating mitigation strategies, consequences in the healthcare and economical system, and pandemic preparedness in terms of early-warning signals for re-emergence of new COVID-19 outbreaks by extended and adapted epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) models

Pandemics: Insurance and Social Protection

This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing in the presence of low probability events, group testing, compositional data analysis for detecting data inconsistencies, behaviouristic aspects in fighting a pandemic, and insurers’ legal problems, amongst others. Concluding with an essay by a practicing actuary on the applicability of the methods proposed, this interdisciplinary book is aimed at actuaries as well as readers with a background in mathematics, economics, statistics, finance, epidemiology, or sociology

Proceedings of the COVid-19 Empirical Research (COVER) Conference: Italy, October 30th, 2020

The Covid-19 pandemic has spread across the world at a rate never seen before, affecting different countries and having a huge impact not only on health care systems but also on economic systems. Never as in this situation the continuous exchange of views between scientists of different disciplines must be considered the keystone to overcome this emergency. The dramatic global situation has prompted many researchers from different fields to focus on studying the Covid-19 pandemic and its economic and social implications in a multi-facet fashion. This volume collects the contributions to the COVid-19 Empirical Research (COVER) Conference, organized by the Centre of Excellence in Economics and Data Science of the Department of Economics, Management and Quantitative Methods, University of Milan, Italy, October 30th, 2020. This conference aimed to collect different points of view by opening an interdisciplinary discussion on the possible developments of the pandemic. The conference contributions ranged in the social, economic and mathematical-statistical areas.illustratorThe Covid-19 pandemic has spread across the world at a rate never seen before, affecting different countries and having a huge impact not only on health care systems but also on economic systems. Never as in this situation the continuous exchange of views between scientists of different disciplines must be considered the keystone to overcome this emergency. The dramatic global situation has prompted many researchers from different fields to focus on studying the Covid-19 pandemic and its economic and social implications in a multi-facet fashion. This volume collects the contributions to the COVid-19 Empirical Research (COVER) Conference, organized by the Centre of Excellence in Economics and Data Science of the Department of Economics, Management and Quantitative Methods, University of Milan, Italy, October 30th, 2020. This conference aimed to collect different points of view by opening an interdisciplinary discussion on the possible developments of the pandemic. The conference contributions ranged in the social, economic and mathematical-statistical areas

PROTECT - A COVID-19 National Core Study: Keeping the UK building safely 2

TransmissionThere is evidence of a broad range of COVID-19 transmission mitigation measures in action; these were generally well received by participants, who reported high levels of compliance.There is evidence of competing requirements of COVID-19 specific and established construction health and safety regulations. For example, and as may be expected, social distancing proved problematic in situations where proximity working is required. Creative measures were used to mitigate risk where social distancingwasn’t feasible for example adapting tools and processes or using teams who cohabited. Whilst participants reported reductions in serious safety incidents on sites, the prevalence of minor incidents increased. For example, face coverings were cited as an inhibitor of effective communication between workers. The availability of remote working arrangements to some construction workers led tosome participants reporting the presence of an ‘us-them’ culture

Promoting Statistical Practice and Collaboration in Developing Countries

"Rarely, but just often enough to rebuild hope, something happens to confound my pessimism about the recent unprecedented happenings in the world. This book is the most recent instance, and I think that all its readers will join me in rejoicing at the good it seeks to do. It is an example of the kind of international comity and collaboration that we could and should undertake to solve various societal problems. This book is a beautiful example of the power of the possible. [It] provides a blueprint for how the LISA 2020 model can be replicated in other fields. Civil engineers, or accountants, or nurses, or any other profession could follow this outline to share expertise and build capacity and promote progress in other countries. It also contains some tutorials for statistical literacy across several fields. The details would change, of course, but ideas are durable, and the generalizations seem pretty straightforward. This book shows every other profession where and how to stand in order to move the world. I urge every researcher to get a copy!" —David Banks from the Foreword Promoting Statistical Practice and Collaboration in Developing Countries provides new insights into the current issues and opportunities in international statistics education, statistical consulting, and collaboration, particularly in developing countries around the world. The book addresses the topics discussed in individual chapters from the perspectives of the historical context, the present state, and future directions of statistical training and practice, so that readers may fully understand the challenges and opportunities in the field of statistics and data science, especially in developing countries. Features • Reference point on statistical practice in developing countries for researchers, scholars, students, and practitioners • Comprehensive source of state-of-the-art knowledge on creating statistical collaboration laboratories within the field of data science and statistics • Collection of innovative statistical teaching and learning techniques in developing countries Each chapter consists of independent case study contributions on a particular theme that are developed with a common structure and format. The common goal across the chapters is to enhance the exchange of diverse educational and action-oriented information among our intended audiences, which include practitioners, researchers, students, and statistics educators in developing countries

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice