189 research outputs found

    Repeatable and reusable research - Exploring the needs of users for a Data Portal for Disease Phenotyping

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    Background: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it hard to compare different study findings and hinders the ability to conduct repeatable and reusable research. Objective: This thesis aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, for both new and existing data portals for phenotypes (concept libraries). Methods: Exploratory sequential mixed methods were used in this thesis to look at which concept libraries are available, how they are used, what their characteristics are, where there are gaps, and what needs to be done in the future from the point of view of the people who use them. This thesis consists of three phases: 1) two qualitative studies, including one-to-one interviews with researchers, clinicians, machine learning experts, and senior research managers in health data science, as well as focus group discussions with researchers working with the Secured Anonymized Information Linkage databank, 2) the creation of an email survey (i.e., the Concept Library Usability Scale), and 3) a quantitative study with researchers, health professionals, and clinicians. Results: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would encourage them to: 1) share their work, such as receiving citations from other researchers; and 2) reuse the work of others, such as saving a lot of time and effort, which they frequently spend on creating new code lists from scratch. They also pointed out several barriers that could inhibit them from: 1) sharing their work, such as concerns about intellectual property (e.g., if they shared their methods before publication, other researchers would use them as their own); and 2) reusing others' work, such as a lack of confidence in the quality and validity of their code lists. Participants suggested some developments that they would like to see happen in order to make research that is done with routine data more reproducible, such as the availability of a drive for more transparency in research methods documentation, such as publishing complete phenotype definitions and clear code lists. Conclusions: The findings of this thesis indicated that most participants valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform such as the CALIBER research platform. Analysis of interviews, focus group discussions, and qualitative studies revealed that different users have different requirements, facilitators, barriers, and concerns about concept libraries. This work was to investigate if we should develop concept libraries in Kuwait to facilitate the development of improved data sharing. However, at the end of this thesis the recommendation is this would be unlikely to be cost effective or highly valued by users and investment in open access research publications may be of more value to the Kuwait research/academic community

    Automatic inference of latent emotion from spontaneous facial micro-expressions

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    Emotional states exert a profound influence on individuals' overall well-being, impacting them both physically and psychologically. Accurate recognition and comprehension of human emotions represent a crucial area of scientific exploration. Facial expressions, vocal cues, body language, and physiological responses provide valuable insights into an individual's emotional state, with facial expressions being universally recognised as dependable indicators of emotions. This thesis centres around three vital research aspects concerning the automated inference of latent emotions from spontaneous facial micro-expressions, seeking to enhance and refine our understanding of this complex domain. Firstly, the research aims to detect and analyse activated Action Units (AUs) during the occurrence of micro-expressions. AUs correspond to facial muscle movements. Although previous studies have established links between AUs and conventional facial expressions, no such connections have been explored for micro-expressions. Therefore, this thesis develops computer vision techniques to automatically detect activated AUs in micro-expressions, bridging a gap in existing studies. Secondly, the study explores the evolution of micro-expression recognition techniques, ranging from early handcrafted feature-based approaches to modern deep-learning methods. These approaches have significantly contributed to the field of automatic emotion recognition. However, existing methods primarily focus on capturing local spatial relationships, neglecting global relationships between different facial regions. To address this limitation, a novel third-generation architecture is proposed. This architecture can concurrently capture both short and long-range spatiotemporal relationships in micro-expression data, aiming to enhance the accuracy of automatic emotion recognition and improve our understanding of micro-expressions. Lastly, the thesis investigates the integration of multimodal signals to enhance emotion recognition accuracy. Depth information complements conventional RGB data by providing enhanced spatial features for analysis, while the integration of physiological signals with facial micro-expressions improves emotion discrimination. By incorporating multimodal data, the objective is to enhance machines' understanding of latent emotions and improve latent emotion recognition accuracy in spontaneous micro-expression analysis

    Electronic Health Record Phenotyping in Cardiovascular Epidemiology

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    The secondary use of EHR data for research is a cost-effective resource for a variety of research questions and domains; however, there are many challenges when using electronic health record (EHR) data for epidemiologic research.This dissertation quantified differences in prevalence for acute myocardial infarction (MI) and heart failure (HF) using phenotyping algorithms differing in diagnosis position of ICD-10-CM codes and the inclusion of clinical components. The period of interest was January 1, 2016 to December 31, 2019 for UNC Clinical Data Warehouse for Health data and October 1, 2015 and December 31, 2019 for Atherosclerosis Risk in Communities (ARIC) Study data, the latter used for validation analyses. During the period of interest, 13,200 acute MI cases and 53,545 HF cases were identified in the UNC data. Age-standardized prevalence of acute MI and HF were highest using Any Diagnosis Position algorithm and lowest for acute MI using 1st or 2nd Diagnosis Position with Lab or Procedure and 1st Diagnosis Position for HF. Projected differences in healthcare expenditures by algorithm as well as patient and clinical characteristics, such as event severity and mortality, were also estimated. When compared to physician-adjudicated hospitalizations in the ARIC study, the phenotyping algorithms used for the UNC analysis performed well given their simplicity. The algorithm with the highest sensitivity was Any Diagnosis Position for acute MI and HF at 75.5% and 70.5%. Specificity, PPV, and NPV ranged from 80-99% for all algorithms. Requiring clinical components had little effect except for increasing PPV slightly, while restricting diagnosis position to 1st or 2nd position decreased sensitivity and increased PPV. The impact of clinical components or diagnosis position did not differ by race, age, or sex subgroups.The results from this dissertation can be used by researchers using EHR data for a variety of reasons from informing their own analytic decisions to validating their study findings. The continued use of EHR data for research requires transparency to facilitate reproducibility as well as studies focused on what we are measuring.Doctor of Philosoph

    The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

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    This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

    Exploring the use of routine healthcare data through process mining to inform the management of musculoskeletal diseases

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    Healthcare informatics can help address some of the challenges faced by both healthcare providers and patients. The medical domain is characterised by inherently complex and intricate issues, data can often be of poor quality and novel techniques are required. Process mining is a discipline that uses techniques to extract insights from event data, generated during the execution of processes. It has had good results in various branches of medical science but applications to musculoskeletal diseases remain largely unexplored. This research commenced with a review of the healthcare and technical literature and applied a variety of process mining techniques in order to investigate approaches to the healthcare plans of patients with musculoskeletal conditions. The analysis involved three datasets from: 1) a private hospital in Boston, US, where data was used to create disease trajectory models. Results suggest the method may be of interest to healthcare researchers, as it enables a more rapid modelling and visualisation; 2) a mobile healthcare application for patients receiving physiotherapy in Sheffield, UK, where data was used to identify possible indicators for health outcomes. After evaluation of the results, it was found that the indicators identified may be down to chance; and 3) the population of Wales to explore knee pain surgery pathways. Results suggest that process mining is an effective technique. This work demonstrates how routine healthcare data can be analysed using process mining techniques to provide insights that may benefit patients suffering with musculoskeletal conditions. This thesis explores how strict criteria for analysis can be performed. The work is intended to expand the breadth of process mining methods available to the data science community and has contributed by making recommendations for service utilisation within physiotherapy at Sheffield Hospital and helped to define a roadmap for a leading healthcare software company

    Music listening at work: control and resistance

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    This thesis investigates the socio-political effects of music listening in the contemporary workplace. Existing studies at the crossroads of music and work cover, on the one hand, the histories of music in the workplace, and on the other, the functional, psychological and scientific nature of music at work. However, few studies to date take a critical sociological approach to music listening in the modern workplace. The following research provides such analysis, informed by theory from both studies of work and employment and music studies. Four case studies were selected for analysis, presenting data from a variety of workplace environments: Amazon warehouse workers, Uber drivers, US and UK postal workers, and commercial truck drivers. For each case study, multiple online forums were combed for data referring to music listening, which was then coded under themes derived from themes arising within existing literature in music and work studies: namely, control, resistance, and musical experience. By contextualising this authentic data from online communities within critical sociology, the aim was to explore the role music listening plays in the politics of the contemporary workplace. Through both qualitative and quantitative analysis of workers’ experience, the study shows that music listening plays a significant role in control and resistance in the workplace. Control is experienced via music listening through concrete rules, technological surveillance, and ideologies of work—specifically ‘common sense’ and ‘customer service’ ideologies. Music listening was also found to play an important role in resistance in the workplace, be that with regards to specific union or otherwise organised action, or acts of misbehaviour and rebellion. In the latter case, music listening technologies such as headphones and aux cables were central to workers’ resistance. Musical experience was also shown to have significant implications for social relations at work: music listening helps people get through the working day because of its effects on the mind and body, while also representing a site of community-building and solidarity-forming, with the online spaces surveyed providing spaces in which workers share, discuss, and debate musical experience

    Nursing and Society

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    The year 2020 is considered by the World Health Organization to be the International Year of the Nurse and Midwife. This book supports the visibility of the contribution of nurses to society. We have included 30 articles on high-quality original research or reviews that provide solid new discoveries that expand current knowledge
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