58,060 research outputs found
Genetic determinants of cortical structure (thickness, surface area and volumes) among disease free adults in the CHARGE Consortium
Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases. We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprised 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank. Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses. We identified genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging
Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration.
Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., GSE2034, GSE2990, and Chanrion's datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine (p=0.03, p=0.13, and p<0.001) and Logistic Regression (p=0.006, p=0.008, and p<0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine (p=0.38, p=0.29, and p=0.047) and Logistic Regression (p=0.38, p=0.04, and p<0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making
The journals of importance to UK clinicians: A questionnaire survey of surgeons
Background: Peer-reviewed journals are seen as a major vehicle in the transmission of research
findings to clinicians. Perspectives on the importance of individual journals vary and the use of
impact factors to assess research is criticised. Other surveys of clinicians suggest a few key journals
within a specialty, and sub-specialties, are widely read. Journals with high impact factors are not
always widely read or perceived as important. In order to determine whether UK surgeons
consider peer-reviewed journals to be important information sources and which journals they read
and consider important to inform their clinical practice, we conducted a postal questionnaire
survey and then compared the findings with those from a survey of US surgeons.
Methods: A questionnaire survey sent to 2,660 UK surgeons asked which information sources
they considered to be important and which peer-reviewed journals they read, and perceived as
important, to inform their clinical practice. Comparisons were made with numbers of UK NHSfunded
surgery publications, journal impact factors and other similar surveys.
Results: Peer-reviewed journals were considered to be the second most important information
source for UK surgeons. A mode of four journals read was found with academics reading more
than non-academics. Two journals, the BMJ and the Annals of the Royal College of Surgeons of England,
are prominent across all sub-specialties and others within sub-specialties. The British Journal of
Surgery plays a key role within three sub-specialties. UK journals are generally preferred and
readership patterns are influenced by membership journals. Some of the journals viewed by
surgeons as being most important, for example the Annals of the Royal College of Surgeons of England,
do not have high impact factors.
Conclusion: Combining the findings from this study with comparable studies highlights the
importance of national journals and of membership journals. Our study also illustrates the
complexity of the link between the impact factors of journals and the importance of the journals
to clinicians. This analysis potentially provides an additional basis on which to assess the role of
different journals, and the published output from research
A Short Review of Ethical Challenges in Clinical Natural Language Processing
Clinical NLP has an immense potential in contributing to how clinical
practice will be revolutionized by the advent of large scale processing of
clinical records. However, this potential has remained largely untapped due to
slow progress primarily caused by strict data access policies for researchers.
In this paper, we discuss the concern for privacy and the measures it entails.
We also suggest sources of less sensitive data. Finally, we draw attention to
biases that can compromise the validity of empirical research and lead to
socially harmful applications.Comment: First Workshop on Ethics in Natural Language Processing (EACL'17
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