Background:
As outbreak of COVID-19 infection, on April 3, 2020, it is stipulated that the number of inpatient companions is limited to one in Taiwan. All companions are required to register their real personal data with 14 days of travel history, occupation, contact history, and cluster history. We would like to evaluate the impact of the new regulations to the accompanying and visiting culture in Taiwan, via analyzing the appearance and characteristics of inpatient companions in this period.
Methods:
Using intelligent technology, we designed a novel system in managing the inpatient companions (InPatients Companions Management System [IPCMS]), and the IPCMS was used to collect data about characteristics of inpatients and companions between April 27 and May 3, 2020. The database is built using MySQL software. Microsoft Excel 2016 and SPSS version 20.0 statistical software were used for data analysis, including the basic data of the companions, differential analysis of companions’ gender, person-days and cumulative time, differential analysis of accompaniment-patient relationship, and frequency of accompaniment and cumulative hours.
Results:
During study period, daily inpatient admissions ranged from 2242 to 2514, the number of companions per day ranged from 2048 to 2293, and the number of companions for one inpatient is 1 to 9 per day, with an average of 1.20 to 1.26. The companions were mostly family members, and most of them were the inpatients’ children (32.9%), and spouse (26.13%). More females than males were noted in all categories of companionship with statistical significance.
Conclusion:
The data obtained in this study could be an important basis for the transformation and reform of the companions culture in Taiwan’s hospitals and will also provide a glimpse into the attitudes and culture of companions who have long been ignorant and neglected. The experience gained in our IPCMS could also serve as a reference for other hospitals in Taiwan and worldwide.
Background:
This study aimed to compare the prediction performance of two-dimensional (2D) and three-dimensional (3D) semantic segmentation models for intracranial metastatic tumors with a volume ≥ 0.3 mL.
Methods:
We used postcontrast T1 whole-brain magnetic resonance (MR), which was collected from Taipei Veterans General Hospital (TVGH). Also, the study was approved by the institutional review board (IRB) of TVGH. The 2D image segmentation model does not fully use the spatial information between neighboring slices, whereas the 3D segmentation model does. We treated the U-Net as the basic model for 2D and 3D architectures.
Results:
For the prediction of intracranial metastatic tumors, the area under the curve (AUC) of the 3D model was 87.6% and that of the 2D model was 81.5%.
Conclusion:
Building a semantic segmentation model based on 3D deep convolutional neural networks might be crucial to achieve a high detection rate in clinical applications for intracranial metastatic tumors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.