765 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Understanding the Big Data Analytics Deployment Gap: Operationally Leveraging Big Data Analytics Capability for Value Generation in Healthcare

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    Despite the surge of big data analytics (BDA) deployments in healthcare, many organizations still struggle to successfully realize value from their investments. This has resulted in the phenomenon of BDA deployment gap, where relative to the interest and investments in BDA initiatives by the organizations, actual value generated from successful migrations of BDA models from data labs to in-practice environment deployments at the initiative level have been scarce. To leverage the growing repository of big data, organizations are required to develop the ability to collect, store, process, and analyze big data (BD); this process is referred to as big data analytics capability (BDAC) in the literature. However, the underlying assumption that organizations with BDAC will always be able to orchestrate the necessary resources and capabilities to use the information from analytics to generate value largely ignores the operational mechanisms involved in how the information is leveraged. This thesis seeks to address this gap in the literature by investigating how organizations find ways to operationally leverage BDAC to generate value in the context of healthcare and generating a better understanding of the knowledge management practices involved in transforming the information from analytics into BDA-enabled capabilities that can lead to improved operational and clinical outcomes. This thesis includes three components. First, the constructs involved in the value generation process from BDAC in the general context are identified: BDA resources, BDAC, and value. Second, a systematic literature review (SLR) is conducted to develop the conceptual framework in the healthcare context and identify the possible constituents of the mediating ‘black box’, which serve as the operational mechanisms in the leveraging process of BDAC in generating value. Finally, a multiple case study is presented to empirically validate the presence and explicate the workings of the ‘black box’ presented in the indirect value generation pathway framework via BDA-enabled functioning capability (BDA-eFC), a dual-purpose capability. The study further supplements the BDAC literature by offering a nuanced understanding of the underlying mechanisms of how organizations implement BDA in the healthcare delivery process at a functional level to generate value, and address the BDA deployment gap in the healthcare context

    The immunobiology of B Lymphocytes in non-small cell lung cancer

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    Lung cancer is the second most diagnosed cancer, after breast cancer, worldwide. However, it is still the leading cause of cancer-specific mortality globally, contributing to 18% of all cancer-related deaths. Non-small cell lung cancer (NSCLC) makes up 85% of lung cancers and dependent on the stage, is amenable to a wide of treatments from surgery to systemic therapy. Immune responses within the tumour microenvironment have increasingly been implicated as determining factors in tumour progression and aggressiveness, and the focus has predominated on T-cell biology. The immune response is a complex interplay between the primary tumour and microenvironment, T and B cells. The role of the B cell in tumour survival is unclear but clearly has a function as tumour infiltration is commonly reported. Through deep phenotyping and multispectral tissue imaging techniques, we identified key differences in the effector and suppressive B cell composition between the tumour and peripheral blood compartments. IL10 positive suppressive B regulatory phenotypes were significantly more abundant in the circulation of patients who recurred post-operatively. Using a broad spectrum immunome array, we employed machine learning techniques and identified an auto-antibody signature in the serum of NSCLC patients that was highly predictive for post-operative recurrence in two independent cohorts. In addition to the techniques described above, we utilised functional ex vivo B cell assays to interrogate the response to checkpoint blockade in advanced disease patients and how this relates to B cell dynamics. Our findings demonstrated that lack of a suppressive B cell “brake” predisposed patients to high grade immune related adverse events post-treatment. Moreover, the B cells from toxicity patients were not only functionally defective in their ability to produce IL10 but also displayed a pan cytokine failure affecting pro-inflammatory cytokines thus suggesting B cell exhaustion in these patients. These findings significantly enhanced our understanding of the aetiopathogenesis of auto-immune toxicity secondary to checkpoint blockade with anti PD-1/PDL-1. In summary, this study aimed to explore the role of B cell biology in NSCLC by employing deep phenotyping and functional assay techniques at the blood and tissue level in both early and advanced stage disease. Our findings are likely to be informative in biomarker development for predicting response to treatment, post-operative relapse and for therapeutic adjuvant polyepitopic vaccine strategies in high-risk patients

    Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

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    Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Internet and Biometric Web Based Business Management Decision Support

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    Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810

    Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges

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    Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process

    Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

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    Background: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks.Objective: This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks.Methods: We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models’ performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation.Results: Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting–based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated.Conclusions: Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption
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