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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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發(fā)表于 2025-3-28 14:37:54 | 只看該作者
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發(fā)表于 2025-3-28 20:10:40 | 只看該作者
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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發(fā)表于 2025-3-29 02:05:20 | 只看該作者
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發(fā)表于 2025-3-29 06:20:39 | 只看該作者
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.
45#
發(fā)表于 2025-3-29 09:37:11 | 只看該作者
46#
發(fā)表于 2025-3-29 14:24:23 | 只看該作者
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
47#
發(fā)表于 2025-3-29 16:31:45 | 只看該作者
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
48#
發(fā)表于 2025-3-29 20:41:28 | 只看該作者
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
49#
發(fā)表于 2025-3-30 01:36:17 | 只看該作者
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.
50#
發(fā)表于 2025-3-30 05:47:23 | 只看該作者
,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
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