Titlebook: Artificial Intelligence in Medicine; 21st International C Jose M. Juarez,Mar Marcos,Allan Tucker Conference proceedings 2023 The Editor(s)

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,Climate and the Grain Price, 1264–1431,such as cough, pain, dyspnoea and anorexia are also present in other diseases. This partly attributes towards the low survival rate. Therefore, it is crucial to screen high risk patients for lung cancer at an early stage through computed tomography (CT) scans. As shown in a previous study, for patie

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Gary R. Hudes MD,Jessie Schol RNhe associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on e

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Amita Patnaik MD,Eric K. Rowinsky MD clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infectio

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The FasL-Fas System in Disease and Therapy, textual radiology reports annotated by humans. Although black-box models predict better on average than interpretable ones, like Cox proportional hazards, they are not inherently understandable. To overcome this trust issue, we introduce time-dependent model explanations into the human-AI decision

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https://doi.org/10.1007/978-3-663-00140-9e associated Explainable Artificial Intelligence (XAI) approaches, which should help to provide insight into the often opaque methods and thus gain trust of users and patients as well as facilitate interdisciplinary work. Using the differentiation of white blood cells with the aid of a high throughp

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