BackgroundDengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN) and artificial neural networks(ANN) can improve prediction accuracy on account of its usage of a large number of parameters for modeling. A hypothesis using a combined scheme of algorithms, including convolutional neural networks(CNN), artificial neural networks(ANN), K-nearest Neighbors Algorithm(KNN), and logis-tical regression(LR), was made to improve the prediction DF accuracy for children. MethodsWe extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF). A 11-variables were eligible by observing the statistical significance in predicting DF risk. The prediction accuracy was based on two training (80%) and testing (20%) sets on model accuracy of the area under the receiver operating characteristic curve (AUC) greater than 0.80 and 0.70, respectively, for discriminating DF+ and DF− in the two sets. Two scenarios of the combined scheme and individual algorithms were compared using the training set to predict the testing set. ResultsWe observed that (i) k-nearest neighbors algorithm has poorer AUC(<0.50), (ii)LR has relatively higher AUC(=0.70), and (ii) the three alternatives have almost equal AUC(=0.68), but smaller than the individual algorithms of NaiveBayes, Logistic regression in raw data and NaiveBayes in normalized data. ConclusionAn LR-based APP was designed to detect DF in children. The 11-item model is suggested to develop the APP for helping patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.
BACKGROUND Objective structured clinical examination (OSCE) has become an acceptable method for evaluating clinical skills in healthcare settings. However, none of the current literature has used visualization plots to compare individual OSCE performances. OBJECTIVE This study uses the Rasch model to (1) compare two other scenarios composed of binary scores and mixed binary and percentage responses; (2) draw a forest plot to visualize students’ performance; and (3) observe the comparison of OCSE scores among subgroups (e.g., schools) using real data.. METHODS We obtained data from 29 medical students who participated in OSCE examinations at 12 stations in 2018. The continuous Rasch model (CRM) was applied to estimate person measures and standard errors (SEs). The characteristics of the percentage CRM were interpreted in comparison to the other two counterparts (i.e., binary and mixed scenarios). Forest plots frequently used in meta-analysis were drawn to display those 29 examinees and five subgroups. RESULTS We used online CRM to draw forest plots and observed that (1) the binary scenario had a wider range of measures, followed by the mixed scenario. The percentage mode as expected has narrower ranges in person abilities(from -0.42 to 0.38) and item difficulties(from -0.48 to 0.41) owing to smaller total raw scores when compared to the other two counterparts;(2) all OSCE performances of medical students do not deviate from the middle line with zero logits; (3) The students in Taipei Medical University outperformed other schools among the five subgroups. CONCLUSIONS We designed two online computer modules for (1) estimating person measures and SEs and (2) drawing forest plots to display the OSCE performance sheets for Taiwanese medical students, which helps hospitals evaluate OSCE performances for students efficiently and objectively, but such results are not limited to medical students in the future.
Background: The h-index has its popularity in the global scientific community. Despite the h-index being used as an indicator of individual research achievement (IRA), two main disadvantages have not been solved: (1) all coauthors contributing equally to article bylines and (2) the integer nature of the h-index making it difficult to differentiate the IRAs among entities. This article evaluated the most cited authors, institutes, and states in United States in ophthalmology in recent ten years using a proposed hx-index.Methods: Authors who worked for departments of ophthalmology in United States were selected for identifying their IRAs in Pubmed Central(PMC) since 2010. Using the PubMed search engine, we conducted an observational study of citation analyses in affiliated research institutes and states of all authors who worked for departments of ophthalmology since 2010. A total of 18,289 published articles from 46,121 authors related to departments of ophthalmology from 50 states were analyzed. The bootstrapping method was applied with an estimated 95% confidence interval (CI) to distinguish the differences in IRAs among states and institutes. The x-index and the Kano model were complemental to the h-index for identifying the group IRA characteristics and rankings. A pyramid plot was used to illustrate the importance of the author-weighted scheme(AWS) used for evaluating IRAs in academics. The hx-index combined both advantages of h-/x-index was proposed to assess IRAs for each facility. A significant difference was identified by observing two bands of estimated 95% CIs that were not overlapped. Furthermore, we drew a choropleth map on Google Maps to visualize the differences of IRA among states.Results: There is a significant rise over time in the number of publications. The top-ranking states in hx-index based on publications and citations were Massachusetts(42.28),California(39.24), and Massachusetts(42.28). If only the top 100 authors were included for calculating the median hx-index, the top three would be California(6.45), Massachusetts(3.97). and New York(3.07) with no significant difference found among these three using the bootstrapping method. The institute and author with the highest hx-index were Harvard Medical School(Massachusetts) and Felipe A Medeiros (California), respectively. We demonstrated that Dr. Medeiros from California published 213 articles in PMC and used the example to elucidate the importance of AWS when IRAs were assessed. Conclusions: With an overall increase in publications in the field of ophthalmology, IRAs assessed by these (1) hx-index, (2)the bootstrapping method, and (3) AWS should be emphasized and promoted more in the future.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.