232 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2022-2023

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    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Knowledge extraction from unstructured data

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    Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Between Ethical Oversight and State Neutrality: Introducing Controversial Technologies into the Public Healthcare Systems of Germany, Italy and England

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    Introducing ethically controversial (bio)technologies into the public healthcare system inevitably provokes societal and legal conflict. While it is often argued that these choices ought to comply with moral standards, the consideration of ethical and religious concerns raises a serious problem of legitimacy. By adopting the position that the state must act in an ethically neutral manner this book provides a critical legal analysis of the relationship between ethics and law and its implications for the public healthcare system. The ensuing examination combines a comparative, legal-constitutional perspective with the investigation of two case studies: preimplantation genetic diagnosis (PGD) and non-invasive prenatal testing (NIPT).Nach welchen Kriterien dürfen ethisch umstrittene (Bio-)Technologien in das öffentliche Gesundheitswesen aufgenommen werden? Zwar wird vertreten, dass diese Entscheidung moralischen Vorgaben entsprechen sollte, doch hat die Berücksichtigung ethischer oder religiöser Bedenken aufgrund des staatlichen Neutralitätsgebots ein Legitimitätsproblem zur Folge. Diese rechtsvergleichende Arbeit untersucht daher kritisch das Verhältnis zwischen Ethik und Recht sowie seine Auswirkungen auf das öffentliche Gesundheitswesen. Insbesondere kombiniert die Analyse rechtsethische und verfassungsrechtliche Ansätze und wendet diese auf zwei Fallbeispiele an, die Präimplantationsdiagnostik (PID) und den nicht-invasiven Pränatalen Test (NIPT)

    Analytics and Intuition in the Process of Selecting Talent

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    In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions
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