Medical student's perception of the COVID-19 pandemic effect on their education and well-being: a cross-sectional survey in the United States.

BACKGROUND: The effects of drastic curricular changes necessitated by the COVID-19 pandemic on medical students' education and wellbeing have remained largely unstudied. Out study aimed to characterize how medical students were affected by the pandemic, specifically how limitations introduced by the pandemic may have affected the quality, delivery, and experience of medical education. METHODS: Three hundred students from 5 U.S. allopathic medical schools were surveyed to determine students' perceptions about their quality of medical education, professional development, and mental health during the COVID-19 pandemic (October 2020-December 2020). RESULTS: A large majority of students report that while lecture-based learning has not been significantly affected by the pandemic, small-group and clinical learning have greatly declined in quality. Students also reported higher levels of depression, anxiety, and uncertainty with regards to their futures as physicians. CONCLUSIONS: The COVID-19 pandemic has greatly affected the medical student education and wellbeing. Although medical schools have implemented measures to continue to train medical students as effectively as they can, further strategies must be devised to ensure the well-being of students in the present and for future national emergencies.

Using Machine Learning of Clinical Data to Diagnose COVID-19

The recent pandemic of Coronavirus Disease 2019 (COVID-19) has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aimed to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID -19 patients and influenza patients based on clinical variables alone. We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement University of California, Office of the President/Tobacco-Related Disease Research Program Emergency COVID-19 Research Seed Funding Grant (R00RG2369) to W.M.O. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: N/A All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.