DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021

In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs. Since there are far more healthy people than infected patients, this classification problem faces the challenge of imbalanced data. To improve the model's ability to recognize minority class (the infected patients), we introduce data augmentation and cost-sensitive methods into our model. Besides, considering the particularity of this task, we deploy some fine-tuning techniques to adjust the pre-training ResNet50. Furthermore, to improve the model's generalizability, we use ensemble learning to integrate prediction results from multiple base classifiers generated using different random seeds. To evaluate the proposed DiCOVA-Net's performance, we conducted experiments with the DiCOVA challenge dataset. The results show that our method has achieved 85.43\% in AUC, among the top of all competing teams.Comment: 5 figure

An improved support vector machine-based diabetic readmission prediction

Cui S, Wang D, Wang Y, Yu P-W, Jin Y. An improved support vector machine-based diabetic readmission prediction. Computer Methods and Programs in Biomedicine. 2018;166:123-135.Background and objective In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes. Method The experiments were conducted from a set of 8756 medical records with 50 different features about diabetic readmission. After preprocessing the data, an effective SMOTE-based method was proposed to solve the imbalance data problem. Further, in order to improve prediction performance, a hybrid feature selection mechanism was devised to select the important features. Subsequently, an improved support vector machine-based (SVM-based) method was developed and the genetic algorithm was used to tune the sensitive parameter of the algorithm. Finally, the five-fold cross-validation method was applied to compare the performance of proposed method with other methods (LACE score, logistic regression, naïve bayes, decision tree and feed forward neural networks). Results Experimental results indicate that the proposed SVM-based method achieves an accuracy of 81.02%, a sensitivity of 82.89%, a specificity of 79.23%, and outperforms other popular algorithms in identifying diabetic patients who may be readmitted. Conclusions Our research can improve the performance of clinic decision support systems for diabetic readmission, by which the readmission possibility as well as the waste of medical resources can be reduced

Angstrom-confined catalytic water purification within Co-TiO x laminar membrane nanochannels

The freshwater scarcity and inadequate access to clean water globally have rallied tremendous efforts in developing robust technologies for water purification and decontamination, and heterogeneous catalysis is a highly-promising solution. Sub-nanometer-confined reaction is the ultimate frontier of catalytic chemistry, yet it is challenging to form the angstrom channels with distributed atomic catalytic centers within, and to match the internal mass transfer and the reactive species’ lifetimes. Here, we resolve these issues by applying the concept of the angstrom-confined catalytic water contaminant degradation to achieve unprecedented reaction rates within 4.6 Å channels of two-dimensional laminate membrane assembled from monolayer cobalt-doped titanium oxide nanosheets. The demonstrated degradation rate constant of the target pollutant ranitidine (1.06 ms−1) is 5–7 orders of magnitude faster compared with the state-of-the-art, achieving the 100% degradation over 100 h continuous operation. This approach is also ~100% effective against diverse water contaminates with a retention time of <30 ms, and the strategy developed can be also extended to other two-dimensional material-assembled membranes. This work paves the way towards the generic angstrom-confined catalysis and unravels the importance of utilizing angstrom-confinement strategy in the design of efficient catalysts for water purification.</p