Titlebook: Explainable AI in Healthcare and Medicine; Building a Culture o Arash Shaban-Nejad,Martin Michalowski,David L. Buc Book 2021 The Editor(s)

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https://doi.org/10.1007/978-1-4899-0682-3sizes of clusters. We attempt at identifying the true stressed and normal clusters using the HRV markers of mental stress reported in the literature. We demonstrate that the clusters produced by the convolutional autoencoders consistently and successfully stratify stressed versus normal samples, as

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The Institutional Structure of Productionge, we extract . utterances—parts of the conversation likely to be cited as evidence supporting some summary sentence. We find that by first filtering for (predicted) noteworthy utterances, we can significantly boost predictive performance for recognizing both diagnoses and RoS abnormalities.

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Normal Frames in Vector Bundles,l and structural patterns. They showed the divergent sensitivities in the spike timing and retweet patterns compared to simulated RandomNet. High self-clustering patterns by governmental and public tweets can hinder efficient communication/information spreading. Epidemic related social media surveil

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Arrigo F. G. Cicero,Alessandro Collettierformance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions’ ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tag

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1860-949X dustry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.978-3-030-53354-0978-3-030-53352-6Series ISSN 1860-949X Series E-ISSN 1860-9503

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A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs,le approach ensures robustness to hyperparameters and therefore TCK. is particularly well suited if there is a lack of labels—a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.

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,Machine Learning Discrimination of Parkinson’s Disease Stages from Walker-Mounted Sensors Data,he results indicate a feasibility of machine learning to accurately classify PD severity stages from kinematic signals acquired by low-cost, walker-mounted sensors and can aid medical practitioners in quantitative assessment of PD progression. The study presents a solution to the small and noisy dat