10,468 research outputs found

    The Hypostasis of the Logos and Informed Consent

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    In the field of bioethics, freedom and autonomy assume the ability of self-regulation and determination. If one has the ability to make one \'s own decisions, then one represents an autonomous and free person. Such a view of autonomy faces many difficulties, because it ultimately limits those who can be considered autonomous. The Orthodox theology of the one Hypostasis of Jesus Christ presents an alternative to such a notions of autonomy, freedom and personhood. A person can be said to possess freedom and autonomy not when she or he can make decisions for herself or himself, but when she or he is perfected in Christ. When people begin to live in communion with one another and with God, their freedom increases. In this essay, the author introduces the Orthodox theology of the two natures of Christ, united to the Divine Hypostasis of the Logos at the moment of the Incarnation, and its implications for human freedom and autonomy

    Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey

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    The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. The aim of this review article is to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.Comment: 13 pages, 3 figure

    Knowledge-based incremental induction of clinical algorithms

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    The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee medical correct structures, or they are not prepared to deal with the incremental arrival of new data. In this thesis we propose a methodology to automatically induce medically correct clinical algorithms (CAs) from hospital databases. These CAs are represented according to the SDA knowledge model. The methodology considers relevant background knowledge and it is able to work in an incremental way. The methodology has been tested in the domains of hypertension, diabetes mellitus and the comborbidity of both diseases. As a result, we propose a repository of background knowledge for these pathologies and provide the SDA diagrams obtained. Later analyses show that the results are medically correct and comprehensible when validated with health care professionals

    Tobacco still a major killer-will we achieve the end game?

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    A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction

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    Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research

    Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)

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    New diagnostic methods and novel therapeutic agents spawn additional and heterogeneous information, leading to an increasingly complex decision-making process for optimal treatment of cancer. A great amount of information is collected in organ-specific multidisciplinary tumor boards (MDTBs). By considering the patient’s tumor properties, molecular pathological test results, and comorbidities, the MDTB has to consent an evidence-based treatment decision. Immunotherapies are increasingly important in today’s cancer treatment, resulting in detailed information that influences the decision-making process. Clinical decision support systems can facilitate a better understanding via processing of multiple datasets of oncological cases and molecular genetic information, potentially fostering transparency and comprehensibility of available information, eventually leading to an optimum treatment decision for the individual patient. We constructed a digital patient model based on Bayesian networks to combine the relevant patient-specific and molecular data with depended probabilities derived from pertinent studies and clinical guidelines to calculate treatment decisions in head and neck squamous cell carcinoma (HNSCC). In a validation analysis, the model can provide guidance within the growing subject of immunotherapy in HNSCC and, based on its ability to calculate reliable probabilities, facilitates estimation of suitable therapy options. We compared actual treatment decisions of 25 patients with the calculated recommendations of our model and found significant concordance (Cohen’s κ = 0.505, p = 0.009) and 84% accuracy
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