Biomedical and Health Sciences
one hundred fifty patients were followed up every year for 28 years, and at each visit the characteristics of the patients were recoreded ( these are the predictors) like: sex( 0= female, 1= male) , age, BMI, LDL-chol, HOMA2-IR, systolic blood pressure and diastolic blood pressure. For each partipant, the recorded value is the mean of the follow up measurements. The age is the median value. The response variables are the transition counts among the states of the process of the disease ( fibrosis in NAFLD ) evolving over time .
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This is the augmentation dataset used in the paper named "LSTformer: Long Short-term Transformer for Real Time Respiratory
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Screening tools play a vital role in sensory processing disorder detection. The automatic collection of features related to behavioral parameters and the response to given stimuli is possible with the recent technology. Real time stress related health parameters are collected as response to visual stimuli created with experts’ suggestions based on visual sensory processing related questionnaire. Body temperature and heart rate are obtained by smart watch.
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Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis.
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Eight participants, sat on a stable chair with no arm rests and a high backrest, with his/her right arm strapped to the passive manipulator. The participant’s motion was simultaneously recorded using a Kinect sensor, an electronic goniometer (Biopac Systems, USA), and a passive marker motion capture system, V120:Trio (OptiTrack, USA). The Kinect sensor was placed 2 m in front and slightly above the participant. The goniometer was attached, using double-sided tape, to the participant’s arm above the elbow. Three reflective markers were used for the V120:Trio recording.
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Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs and affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Currently, traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently visit laboratories. Therefore, anemia diagnosis using noninvasive and cost effective methods is an open challenge.
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These datasets are used for epidemilogical modeling using artifical neural network.
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This dataset is the Cardiopulmonary Exercise Test(CPET) processed before using machine learning algorithms. The CPET cases went to a diverse feature engineering process that gives over 100 features and 4 labels. The labels are in binary and define if the patient has one of the following conditions, healthy, primary cardiac, pulmonary or other limitation.
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The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.
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This is a protein negative interaction dataset, generated by our proposed method the “Features Dissimilarity-based Negative Generation” approach to generate protein negative sampling based on sequence data. It measures similarity of sequence characteristics without alignment based on Protein similarity. It achieved results of 97% compared to randomly generated negative dataset.
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