Objective: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. Methods: We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. Results: Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. Conclusion: The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Ultrasound examination has become a vital mid-term prenatal screening method due to its numerous benefits. However, identifying fetal ultrasound sections poses significant challenges and requires the expertise of experienced doctors, especially when dealing with unfavorable fetal positions and image artifacts. The shortage of skilled doctors and the complexity of these screenings highlight the necessity of artificial intelligence to support medical professionals. Most current research targets a few specific fetal ultrasound sections for particular tasks. In reality, mid-pregnancy level II ultrasound assessments involve around ten different fetal sections. Therefore, there is a critical need for algorithms that can recognize these level II fetal sections. Furthermore, the standard ultrasound planes located in the same region exhibit a high degree of similarity. The traditional fetal slice algorithm such as ResNet generates feature maps that are sparse matrices. As a result, employing global average pooling (GAP) layers can lead to significant information loss, making it challenging to effectively differentiate between similar views. In this study, we introduce a deep-learning network designed for second-trimester anatomy ultrasound standard plane recognition. Our approach incorporates a novel spectral pooling paradigm. We introduce singular pooling to enhance information extraction from feature maps and integrate this singular pooling layer into ResNet. This methodology is tested on our dataset, which includes ten distinct standard fetal ultrasound sections from second-trimester level II examinations. Our model achieves exceptional performance, with an accuracy of 92.07% and an F1-Score of 0.919. The implementation of singular pooling significantly improves the original ResNet model. This method can effectively aid in the identification of standard plane recognition in second-trimester anatomy ultrasounds. Furthermore, we validated the efficacy of our model by applying it to two additional datasets, thereby further demonstrating its efficiency and applicability. Our code is available at https://github.com/sysll/vectore/tree/ master.

In a large-scale MIMO system, the enormous computational complexity makes traditional antenna selection algorithms hard to apply effectively as the number of antennas increases. This paper proposes a specific 13-layer residual neural network model called ResidualJointAntennaNet for the selection of joint transmit-receive antennas to solve this problem. To improve the performance of the multi-category label generation process, we utilize the exhaustive search algorithm to generate two types of One-Hot Encoding labels for all channel matrices. The labels are generated according to two maximization criteria, the channel capacity criterion and the channel gain criterion, respectively. Then we used the large-scale antenna data to train ResidualJointAntennaNet. Consequently, the trained model can assign labels to each channel matrix in the test set. The assigned label represents the optimal antenna subset that corresponds to the best communication performance. Experimental results show that the proposed method achieves near-optimal communication performance with low computational cost in real-time antenna selection.

The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we proposed two automatic diagnosis systems for non-invasive diagnosis of chronic hepatitis C based on serum indices, an extreme learning machine (ELM) based auto-diagnosis method and a hybrid method using k-means clustering and ELM. The two proposed systems were used to predict the fibrosis stage and inflammatory activity grade of patients with chronic hepatitis C by analyzing their serum index observations. ELM has superiorities such as simple structure and fast calculation speed and can provide good diagnosis performance. To overcome the problem of class-imbalance, outliers and small sample size, we also proposed a method hybridizing k-means and ELM. It employed the k-means clustering to generate new robust training samples and then employed the new generated training samples to train an ELM for chronic hepatitis C diagnosis. The proposed methods were tested on 123 real clinical cases. Experimental results show that the proposed methods outperform the state-of-the-art methods for the fibrosis stage and inflammatory activity grade diagnosis tasks.