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Results of Mindfulness-Based Stress Reduction in Wellness Cultural Care Education and learning: any Cohort-Controlled Examine.
Early prediction of sepsis is essential to give the patient timely treatment since each hour of delayed treatment has been associated with an increase in mortality. Current sepsis detection systems rely on empirical Clinical Decision Rules(CDR)s, which are based on vital signs that can be collected from the bedside. The main disadvantages of CDRs include questions of generalizability and performance variance when applied to the populations different from the groups used for derivation and often take years to develop and validate. This paper proposes a deep learning model using Bi-Directional Gated Recurrent Units(GRU), which uses a wide range of parameters that are associated with vitals, laboratory, and demographics of patients. The proposed model has an area under the receiver operating characteristic (AUROC) of 0.97, outperforming all the existing systems in the current literature. The model can handle the missing data, and irregular sampling intervals frequently present in medical records.Clinical relevance-The proposed model can be used to predict the onset of sepsis 6 hours ahead of time by the use of a machine learning algorithm. This proposed method outperforms the sepsis prediction machine learning models found in the current literature.This study analyzed the selective attention processing related to cognitive load on simultaneous interpretation (SI). We tested simultaneous interpreter's brain function using EEG signals and calculated inter-trial coherence (ITC) extracted by the 40-Hz auditory steady-state response (ASSR). TNIK&MAP4K4-IN-2 In this experiment, we set two conditions as Japanese-English translation and Japanese shadowing cognition. We also compared two subject groups S rank with more than 15 years of SI experience (n=7) and C rank with less than one year experience (n=15). As a result, the ITCs for S rank in interpreting conditions were more significantly increased than C rank in the shadowing conditions (ITC p less then 0.001). Our results demonstrate that 40-Hz ASSR might be a good indicator of selective attention and cognitive load during SI in ecologically valid environmental conditions. It can also be used to detect attention and cognitive control dysfunction in ADHD or schizophrenia.Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.While machine learning algorithms are able to detect subtle patterns of interest in data, expert knowledge may contain crucial information that is not easily extracted from a given dataset, especially when the latter is small or noisy. In this paper we investigate the suitability of Gaussian Process Classification (GPC) as an effective model to implement the domain knowledge in an algorithm's training phase. Building on their Bayesian nature, we proceed by injecting problem- specific domain knowledge in the form of an a-priori distribution on the GPC latent function. We do this by extracting handcrafted features from the input data, and correlating them to the logits of the classification problem through fitting a prior function informed by the physiology of the problem. The physiologically-informed prior of the GPC is then updated through the Bayes formula using the available dataset. We apply the methods discussed here to a two-class classification problem associated to a dataset comprising Heart Rate Variability (HRV) and Electrodermal Activity (EDA) signals collected from 26 subjects who were exposed to a physical stressor aimed at altering their autonomic nervous systems dynamics. We provide comparative computational experiments on the selection of appropriate physiologically-inspired GPC prior functions. We find that the recognition of the presence of the physical stressor is significantly enhanced when the physiologically-inspired prior knowledge is injected into the GPC model.
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