The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.
BACKGROUND
Within the literature, there has been limited research tracking the career trajectories of international medical graduates (IMGs) following residency training.
OBJECTIVE
To compare the characteristics of IMG and US medical school graduate (USMG) neurosurgeons holding academic positions in the United States and also analyze factors that influence IMG career trajectories following US-based residency training.
METHODS
We collected data on 243 IMGs and 2506 USMGs who graduated from Accreditation Council for Graduate Medical Education (ACGME)-accredited neurosurgery residency programs. We assessed for significant differences between cohorts, and a logistic regression model was used for the outcome of academic career trajectory.
RESULTS
Among the 2749 neurosurgeons in our study, IMGs were more likely to pursue academic neurosurgery careers relative to USMGs (59.7% vs 51.1%; P = .011) and were also more likely to complete a research fellowship before beginning residency (odds ratio [OR] = 9.19; P < .0001). Among current US academic neurosurgeons, USMGs had significantly higher pre-residency h-indices relative to IMGs (1.23 vs 1.01; P < .0001) with no significant differences between cohorts when comparing h-indices during (USMG = 5.02, IMG = 4.80; P = .67) or after (USMG = 14.05, IMG = 13.90; P = .72) residency. Completion of a post-residency clinical fellowship was the only factor independently associated with an academic career trajectory among IMGs (OR = 1.73, P = .046).
CONCLUSION
Our study suggests that while IMGs begin their US residency training with different research backgrounds and achievements relative to USMG counterparts, they attain similar levels of academic productivity following residency. Furthermore, IMGs are more likely to pursue academic careers relative to USMGs. Our work may be useful for better understanding IMG career trajectories following US-based neurosurgery residency training.
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.