Background New sonographic quality criteria to optimize optic nerve sheath diameter (ONSD) measurements were suggested. The latter were correlated to elevated intracranial pressure (ICP) in traumatic brain injury (TBI). Aim We investigated whether ONSD measurements were correlated to simultaneous ICP measurements in severe TBI. Methods Forty patients with severe TBI (Marshall Scale ≥II and GCS ≤8) participated in the study. All patients had an intraparenchymal ICP catheter inserted, while ONSD was measured bilaterally, upon admission and over the next 48 hours, based on the new sonographic criteria. A total of 400 ONSD measurements were performed, while mean ONSD values of both eyes were used in the analysis. Results ONSD measurements were strongly correlated to ICP values (r=0.74, p < 0.0001). Receiver operator curve (ROC) analysis revealed that the ONSD cutoff value for predicting elevated ICP was 6.4 mm when using the mean of both eyes (AUC = 0.88, 95% CI = 0.80 to 0.95; sensitivity = 85.3%, specificity = 82.6%). Linear regression analysis nested models revealed that sex (p=0.006) and height (p=0.04) were significant predictors of ONSD values. Conclusion When applying the new sonographic quality criteria, ONSD is strongly correlated to ICP in severe TBI. Whether to use such criteria to monitor ONSD as a proxy for ICP trend in TBI remains to be further explored.
In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.
We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.
Background High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling. Methods A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices. Results We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. Conclusions There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.
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