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In this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.
Accurate prognostication is important for patients and their families to prepare for the end of life. Objective Prognostic Score (OPS) is an easy-to-use tool that does not require the clinicians’ prediction of survival (CPS), whereas Palliative Prognostic Score (PaP) needs CPS. Thus, inexperienced clinicians may hesitate to use PaP. We aimed to evaluate the accuracy of OPS compared with PaP in inpatients in palliative care units (PCUs) in three East Asian countries.
This study was a secondary analysis of a cross-cultural, multicenter cohort study. We enrolled inpatients with far-advanced cancer in PCUs in Japan, Korea, and Taiwan from 2017 to 2018. We calculated the area under the receiver operating characteristics (AUROC) curve to compare the accuracy of OPS and PaP.
A total of 1,628 inpatients in 33 PCUs in Japan and Korea were analyzed. OPS and PaP were calculated in 71.7% of the Japanese patients and 80.0% of the Korean patients. In Taiwan, PaP was calculated for 81.6% of the patients. The AUROC for 3-week survival was 0.74 for OPS in Japan, 0.68 for OPS in Korea, 0.80 for PaP in Japan, and 0.73 for PaP in Korea. The AUROC for 30-day survival was 0.70 for OPS in Japan, 0.71 for OPS in Korea, 0.79 for PaP in Japan, and 0.74 for PaP in Korea.
Significance of results
Both OPS and PaP showed good performance in Japan and Korea. Compared with PaP, OPS could be more useful for inexperienced physicians who hesitate to estimate CPS.
While previous studies have described career outcomes of physician-scientist trainees after graduation, trainee perceptions of research-intensive career pathways remain unclear. This study sought to identify the perceived interests, factors, and challenges associated with academic and research careers among predoctoral MD trainees, MD trainees with research-intense (>50%) career intentions (MD-RI), and MD-PhD trainees.
A 70-question survey was administered to 16,418 trainees at 32 academic medical centers from September 2012 to December 2014. MD vs. MD-RI (>50% research intentions) vs. MD-PhD trainee responses were compared by chi-square tests. Multivariate logistic regression analyses were performed to identify variables associated with academic and research career intentions.
There were 4433 respondents (27% response rate), including 2625 MD (64%), 653 MD-RI (15%), and 856 MD-PhD (21%) trainees. MD-PhDs were most interested in pursuing academia (85.8%), followed by MD-RIs (57.3%) and MDs (31.2%). Translational research was the primary career intention for MD-PhD trainees (42.9%). Clinical duties were the primary career intention for MD-RIs (51.9%) and MDs (84.2%). While 39.8% of MD-PhD respondents identified opportunities for research as the most important career selection factor, only 12.9% of MD-RI and 0.5% of MD respondents shared this perspective. Interest in basic research, translational research, clinical research, education, and the ability to identify a mentor were each independently associated with academic career intentions by multivariate regression.
Predoctoral MD, MD-RI, and MD-PhD trainees are unique cohorts with different perceptions and interests toward academic and research careers. Understanding these differences may help to guide efforts to mentor the next generation of physician-scientists.
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