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Titlebook: Les techniques de monitorage hémodynamique en réanimation; Jean-Louis Teboul,Daniel Backer Book 2007Latest edition Springer-Verlag Paris 2

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發(fā)表于 2025-3-25 05:48:58 | 只看該作者
22#
發(fā)表于 2025-3-25 07:37:33 | 只看該作者
s accessibles par les techniques actuelles de monitorage hémodynamique. Il vise ainsi à ce que l’utilisation de ces techniques soit parfaitement ma?trisée par les réanimateurs et anesthésistes-réanimateurs afin que la prise en charge des patients soit in fine optimale..
23#
發(fā)表于 2025-3-25 14:53:52 | 只看該作者
ial computing; neural networks; nature inspired computing and optimization; genetic algorithms; signal processing; pattern recognition; biometrics recognition; image processing; information security; virtual reality and human-computer interaction; healthcare informatics theory and methods; artificia
24#
發(fā)表于 2025-3-25 19:02:16 | 只看該作者
ed in each subclass. Each module was analyzed by biological enrichment, classification performance and survival performance and also was validated by miRspongR. The experimental results indicate that BCLMM has excellent performance to explore regulatory modules.
25#
發(fā)表于 2025-3-25 21:14:12 | 只看該作者
J. -L. Teboul,D. De Backerrest (RF) classifier for accurate classification. In the five-fold cross-validation on . and . data sets, GCNSP achieved 93.65% and 90.69% prediction accuracy with 99.64% and 99.08% specificity, respectively. In comparison with different classifier models and other existing methods, GCNSP shows stro
26#
發(fā)表于 2025-3-26 04:11:17 | 只看該作者
27#
發(fā)表于 2025-3-26 06:07:52 | 只看該作者
28#
發(fā)表于 2025-3-26 08:31:38 | 只看該作者
ethod. The .-norm sparse regularization feature selection method can be used to select features that are crucial for discrimination, and then explore the abnormal functional connections of schizophrenia with fMRI data. Our results showed that the abnormal functional connections are related to superi
29#
發(fā)表于 2025-3-26 15:32:36 | 只看該作者
30#
發(fā)表于 2025-3-26 19:41:44 | 只看該作者
F. Michard,J. -L. Teboul this paper, we develop a group based computational model of Bayesian disease-oriented ranking for inferring the most potential microbes associated with human diseases. It is the first attempt to predict this kind of associations by using 16S rRNA gene sequences. Based on the sequence information of
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