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Quantification of Albumin and ɣ-Globulin Concentrations by Multivariate Regression Based on Admittance Relaxation Time Distribution (mrARTD).
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Dept. Mechanical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba, 263-8522, JAPAN.
Albumin and γ-globulin concentrations in subcutaneous adipose tissues (SAT) have been quantified by multivariate regression based on admittance relaxation time distribution (mraRTD) under the fluctuated background of sodium electrolyte concentration. The mraRTD formulates P = Ac + Ξ (P: peak matrix of distribution function magnitude ɣP and frequency τP, c: concentration matrix of albumin cAlb, γ-globulin Gloc, and sodium electrolyte Nac, A: coefficient matrix of a multivariate regression model, and Ξ: error matrix). The mraRTD is implemented by two processes which are: 1) the training process of A through the maximum likelihood estimation of P and 2) the quantification process of cAlb, Gloc, and Nac through the model prediction.
View Article and Find Full Text PDFMachine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.
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Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Background: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice.
View Article and Find Full Text PDFPsychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.
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Smith School of Business, Queen's University, Kingston, ON, Canada.
Background: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
View Article and Find Full Text PDFIncorporating area-level social drivers of health in predictive algorithms using electronic health record data.
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Department of Health Policy, Stanford School of Medicine, Stanford, CA 94305, United States.
Objectives: The inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do.
View Article and Find Full Text PDFA deep learning tissue classifier based on differential Co-expression genes predicts the pregnancy outcomes of cattle.
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Inner Mongolia SK·Xing Animal Breeding and Breeding Biotechnology Research Institute Co., Ltd, Hohhot 011517, China.
Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant).
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