objective
This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic.
materials and methods
The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications.
results
The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states.
conclusion
These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.
Background:We hypothesize that being an editorial board member (EBM) in a high impact factor specialty medical journal increases the chances of publishing in the same journal.Materials and Methods:The publication trends of the first five EBMs in the five highest impact factor Anesthesiology and Gastroenterology journals were analyzed. Preceding 5 years' publications appearing on PubMed were grouped into as follows: number of publications in the journal in which the EBM serves (N1), number of publications by the same author in the other four highest impact factor (IF) journals (N2) and number of publications in all the other journals (N3). We evaluated the probability of the observed distribution of publications in the five highest IF journals happening by chance alone, assuming that all the EBMs had the same opportunity of publishing in any of these journals. The probability of publishing in their own journal was assumed to be one fifth.Results:The EBMs published their manuscripts in their own journal at a very high frequency. Encompassing all ten journals, the calculated P value for such a distribution was <0.001. In two journals, Anesthesia and Analgesia and Anaesthesia, the EBMs' publications in their journal were more than twice the cumulative total in the remaining four journals. In three of the five gastroenterology journals analyzed, combined publications of the five EBMs were greater in their own journal than the remaining four journals combined.Conclusions:Despite proclaimed fair peer review process, EBMs seem to get preference in their own journals.
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