261 research outputs found

    Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

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    Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification

    Cyberbullying in educational context

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    Kustenmacher and Seiwert (2004) explain a man’s inclination to resort to technology in his interaction with the environment and society. Thus, the solution to the negative consequences of Cyberbullying in a technologically dominated society is represented by technology as part of the technological paradox (Tugui, 2009), in which man has a dual role, both slave and master, in the interaction with it. In this respect, it is noted that, notably after 2010, there have been many attempts to involve artificial intelligence (AI) to recognize, identify, limit or avoid the manifestation of aggressive behaviours of the CBB type. For an overview of the use of artificial intelligence in solving various problems related to CBB, we extracted works from the Scopus database that respond to the criterion of the existence of the words “cyberbullying” and “artificial intelligence” in the Title, Keywords and Abstract. These articles were the subject of the content analysis of the title and, subsequently, only those that are identified as a solution in the process of recognizing, identifying, limiting or avoiding the manifestation of CBB were kept in the following Table where we have these data synthesized and organized by years

    Trustworthy Federated Learning: A Survey

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    Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    IoT Health Devices: Exploring Security Risks in the Connected Landscape

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    The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century

    Unlocking ecological history using fish remains: Eco-evolutionary consequences of exploitation in the Atlantic bluefin tuna

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    During recent decades, the health of ocean ecosystems and fish populations has been threatened by overexploitation, pollution, and anthropogenic-driven climate change. Due to a lack of long-term data, we have a poor understanding of when intensive exploitation began and what impact anthropogenic activities have had on the ecology and evolution of fishes. Such information is crucial to recover degraded and depleted marine ecosystems and fish populations, maximise their productivity in-line with historical levels, and predict their future dynamics. In this thesis, I evaluate anthropogenic impacts on the iconic Atlantic bluefin tuna (Thunnus thynnus; BFT), one of the longest and recently most intensely exploited marine fishes, with a tremendous cultural and economic importance. Using a long-time series of archaeological and archived faunal remains (bones) dating back to approximately two millennia ago, I apply morphological, isotopic, and genomic techniques to perform the first studies on long-term BFT size and growth, diet and habitat use, and demography and adaptation, and produce the first genome-wide data on this species. My findings suggest that exploitation had impacted BFT foraging behaviour by the ~16th century when coastal ecosystem degradation induced a pelagic shift in diet and habitat use. I reveal that BFT biomass began to decline much earlier than hitherto documented, by the 19th century, consistent with intensive tuna trap catches during this period and catch-at-size increasing. I find that BFT juvenile growth had increased by the early 1900s (and more dramatically by the 21st century) which may reflect an evolutionary response to size selective harvest–which I find putative genomic signatures of. Further, I observed that BFT foraging behaviours have been modified following overexploitation during the 20th century, which previously included a isotopically distinct, Black Sea niche. Finally, I show that despite biomass declining from centuries ago, BFT has retained genomic diversity

    G-Quadruplex Reporters: Structural Studies and Application for Visual and Fluorescent Detection of Point Mutations in Nucleic Acid Sequences

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    DNA-based diagnostics traditionally utilize hybridization probes: strands of DNA complimentary to a target sequence that, upon binding, generate a signal to indicate the presence of the target. The classic hybridization probes are the molecular beacon (MB) and TaqMan probes, both single DNA strands with a fluorophore and quencher at opposing ends. Despite their widespread use in applications such as qPCR due to their ability to multiplex with a variety of bound fluorophores, these probes have several shortcomings: temperature limitations for selective target recognition and differentiating single-nucleotide polymorphisms, intrinsic design limitations to interrogate some target sequences, and a high relative cost for synthesis and purification. A split probe design in combination with label-free reporters overcomes these shortcomings. Split probes break the target-recognition sequence into two shorter pieces, each equipped with a portion of a signal reporting unit. The shorter length both allows for the probes to efficiently function at ambient temperatures, opposed to the elevated temperatures generally required for monomer probes, and provides greater ability to discriminate single-nucleotide polymorphisms. These structures will only generate a signal if complete, which may only occur when both halves of the probe are properly bound to the target. By utilizing label-free reporters such as light-up aptamers and/or (deoxy)ribozymes, split probes offer cost-efficiency advantages over other fluorescent probes. In this dissertation, advances in the usage of split probes with G-quadruplex-based signal transducing units are detailed. An alphanumeric display comprised of tandem probes utilizing the peroxidase-like deoxyribozyme for colorimetric output demonstrates the instrument-free usage of these systems. Additionally, the promiscuous activity of the dapoxyl light-up aptamer with a variety of arylmethane, offers a label-free fluorescent split probe that is capable of discerning single-nucleotide polymorphisms without expensive chemical modifications

    Análise bioética do uso de big data em saúde

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    Dissertação (mestrado) — Universidade de Brasília, Faculdade de Ciências da Saúde, Programa de Pós-Graduação em Bioética, 2022.A inédita capacidade de análise e predição das tecnologias de Big Data é bastante promissora no campo da saúde. O uso massivo de dados pessoais, no entanto, traz riscos individuais e coletivos significativos. Esta dissertação tem por objetivo compreender o presente estado da reflexão ética sobre o uso de Big Data em saúde e as principais implicações já identificadas. Trata-se de estudo comparativo entre o Relatório sobre Big Data e Saúde do Comitê Internacional de Bioética (IBC) da Organização das Nações Unidas para a Educação, a Ciência e a Cultura (Unesco) e a literatura científica nacional, regional e internacional sobre o tema. A análise dos artigos foi feita com apoio da ferramenta IRaMuTeQ. Como resultado, observou-se que o Relatório identifica como os principais aspectos éticos das novas tecnologias: a autonomia, a privacidade e o equilíbrio entre interesses individuais e coletivos. A literatura, por sua vez, é ainda incipiente, mas já aponta dimensões digitais (técnicas), sociais, econômicas e regulatórias com aspectos éticos importantes. Dentre eles figuram: a incerteza e imprevisibilidade, opacidade, justiça e desigualdade, consentimento, modulação do comportamento, validade e apropriação do conhecimento, e privacidade e confidencialidade, entre outros. O Relatório introduz as principais questões éticas levantadas pelo tema, incluindo a justiça e não discriminação e a questão ambiental, esta última ausente na literatura analisada. O IBC, no entanto, não tratou de temas como as alterações na condição humana, decorrentes da intensificação da socialização digital, e a modulação comportamental dos usuários. Contudo, é a ausência da discussão a respeito da comoditização da privacidade que mais se faz sentir no Relatório. O conceito de comoditização de privacidade proposto por Zuboff evidencia a expropriação e transformação da privacidade em ativo financeiro. Sem controle social e governança democrática, o fenômeno pode se tornar o principal responsável pelo incremento futuro das desigualdades sociais já existentes. A presente dissertação se propõe a contribuir para o desenvolvimento de reflexão crítica e pautada em contextos regionais periféricos, praticamente nula na amostra analisada, por considerar este um dos instrumentos necessários para um uso mais justo dessas novas tecnologias.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The unprecedented analytical and predictive power of Big Data technologies holds great promise in the field of healthcare. The massive use of personal data, however, brings significant individual and collective risks. This dissertation aims to understand the present state of ethical thinking on the use of Big Data in healthcare and the major implications already identified. This is a comparative study between the Report on Big Data and Health of the International Bioethics Committee (IBC) of the United Nations Educational, Scientific and Cultural Organization (Unesco) and the national, regional and international scientific literature on the subject. The analysis of the articles was done with the support of the IRaMuTeQ tool. As a result, it was observed that the Report identifies as the main ethical aspects of new technologies: autonomy, privacy, and balance between individual and collective interests. The literature, in turn, is still incipient, but already points out digital (technical), social, economic, and regulatory dimensions with important ethical aspects. These include uncertainty and unpredictability, opacity, fairness and inequality, consent, behavior modulation, validity and appropriation of knowledge, and privacy and confidentiality, among others. The Report introduces the main ethical issues raised by the topic, including justice and nondiscrimination and the environmental issue, the latter absent in the literature reviewed. The IBC, however, did not address issues such as changes in the human condition, arising from the intensification of digital socialization, and the behavioral modulation of users. However, it is the lack of discussion regarding the commodification of privacy that is the most felt absence in the Report. The concept of commodification of privacy proposed by Zuboff highlights the expropriation and transformation of privacy into a financial asset. Without social control and democratic governance, the phenomenon may become the main responsible for the future increase in existing social inequalities. The present dissertation proposes to contribute to the development of critical and guided reflection in peripheral regional contexts, practically null in the sample analyzed, for considering this one of the necessary instruments for a fairer use of these new technologies

    Intelligent Biosignal Analysis Methods

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    This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others
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