Improving Continual Relation Extraction through Prototypical Contrastive Learning

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL's advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL's contrastive learning on improving CRE performance

Leveraging Frequency Domain Learning in 3D Vessel Segmentation

Coronary microvascular disease constitutes a substantial risk to human health. Employing computer-aided analysis and diagnostic systems, medical professionals can intervene early in disease progression, with 3D vessel segmentation serving as a crucial component. Nevertheless, conventional U-Net architectures tend to yield incoherent and imprecise segmentation outcomes, particularly for small vessel structures. While models with attention mechanisms, such as Transformers and large convolutional kernels, demonstrate superior performance, their extensive computational demands during training and inference lead to increased time complexity. In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models, which can reduce computational expenses while preserving global receptive fields within the network. Furthermore, a zero-parameter frequency domain fusion method is designed to improve the skip connections in U-Net architecture. Experimental results on a public dataset and an in-house dataset indicate that our novel Fourier transformation-based network achieves remarkable dice performance (84.37\% on ASACA500 and 80.32\% on ImageCAS) in tubular vessel segmentation tasks and substantially reduces computational requirements without compromising global receptive fields

Decision Support System to Risk Stratification in the Acute Coronary Syndrome Using Fuzzy Logic

Acute coronary syndrome (ACS) is a set of symptoms and signs which define a range of conditions related with the unexpected reduced blood flow to the heart. In ACS, the heart muscles cannot function properly due to the decrease of blood flow. Myocardial infarction (MI) is a condition which comes under the umbrella of acute coronary syndrome. The aim of risk stratification (RS) in ACS is to recognize patients at high risk of ischemic events. Yet, no investigative study is available to identify the patients at high risk. Therefore, to facilitate this process, it would be ideal to have a reliable and trustworthy method by the help of which the doctors can make early and easy decisions for the patient and for detecting the related disease. This research used the features of GRACE Score to RS in the ACS and presented decision support system (DSS). The concept of probabilistic approach has been used as a tool to model the identified features for decision-making (DM). This technique can be further used for DM purposes to RS in the ACS in healthcare. Furthermore, the result of the proposed method has proved closer and more reliable DM of patient and then eventually can be used for advice of medicine and rest accordingly by the doctors

Closed-loop transcutaneous auricular vagus nerve stimulation for the improvement of upper extremity motor function in stroke patients: a study protocol

BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) has garnered attention for stroke rehabilitation, with studies demonstrating its benefits when combined with motor rehabilitative training or delivered before motor training. The necessity of concurrently applying taVNS with motor training for post-stroke motor rehabilitation remains unclear. We aimed to investigate the necessity and advantages of applying the taVNS concurrently with motor training by an electromyography (EMG)-triggered closed-loop system for post-stroke rehabilitation.MethodsWe propose a double-blinded, randomized clinical trial involving 150 stroke patients assigned to one of three groups: concurrent taVNS, sequential taVNS, or sham control condition. In the concurrent group, taVNS bursts will synchronize with upper extremity motor movements with EMG-triggered closed-loop system during the rehabilitative training, while in the sequential group, a taVNS session will precede the motor rehabilitative training. TaVNS intensity will be set below the pain threshold for both concurrent and sequential conditions and at zero for the control condition. The primary outcome measure is the Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Secondary measures include standard upper limb function assessments, as well as EMG and electrocardiogram (ECG) features.Ethics and disseminationEthical approval has been granted by the Medical Ethics Committee, affiliated with Zhujiang Hospital of Southern Medical University for Clinical Studies (2023-QX-012-01). This study has been registered on ClinicalTrials (NCT05943431). Signed informed consent will be obtained from all included participants. The findings will be published in peer-reviewed journals and presented at relevant stakeholder conferences and meetings.DiscussionThis study represents a pioneering effort in directly comparing the impact of concurrent taVNS with motor training to that of sequential taVNS with motor training on stroke rehabilitation. Secondly, the incorporation of an EMG-triggered closed-loop taVNS system has enabled the automation and individualization of both taVNS and diverse motor training tasks—a novel approach not explored in previous research. This technological advancement holds promise for delivering more precise and tailored training interventions for stroke patients. However, it is essential to acknowledge a limitation of this study, as it does not delve into examining the neural mechanisms underlying taVNS in the context of post-stroke rehabilitation

Development and external validation of a nomogram for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage

BackgroundPostoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP.MethodsAn internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.ResultsAmong patients in our internal cohort, 15.66% (n = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively.ConclusionWe found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population

Efficacy and safety of anterior transposition of the ulnar nerve for distal humerus fractures: A systematic review and meta-analysis

BackgroundThis systematic review and meta-analysis was performed to summarize available evidence of anterior transposition of the ulnar nerve for patients with distal humerus fractures.Materials and MethodsThe databases were searched from PubMed, Cochrane, Embase, Scopus, Web of Science, Chinese National Knowledge Infrastructure (CNKI), Chongqing VIP Database (VIP), and Wan Fang Database up to June 2022. The clinical outcome included operation time, fracture healing time, hospital stays, elbow joint function, and ulnar neuritis rate. Statistical analysis was performed with Review Manager 5.3 (Cochrane Collaboration).ResultsA total of 17 studies were included (8 RCTs and 9 retrospective studies), and 1280 patients were analyzed. The results of this meta-analysis showed anterior transposition group had longer operation time (MD = 20.35 min, 95%CI: 12.56–28.14, P < 0.00001). There was no significant difference in fracture healing time (SMD = −0.50, 95%CI: −1.50–0.50, P = 0.33), hospital stays (MD = −1.23 days, 95%CI: −2.72–−0.27, P = 0.11), blood loss (MD = 2.66 ml, 95%CI: −2.45–7.76, P = 0.31), and ulnar neuritis rate (OR = 1.23, 95%CI: 0.63–2.42, P = 0.54) between two groups. Finally, elbow joint motion, elbow joint function, fracture nonunion, and post-operative infection (P > 0.05) between two groups were not significantly statistic difference.ConclusionThis meta-analysis showed that anterior transposition group is not superior to non-transposition group for patients with distal humerus fractures without ulnar nerve injury. On the contrary, non-transposition group have shorter operation time than that of anterior transposition group. Non-transposition group did not increase the post-operative ulnar neuritis rate. Therefore, both anterior transposition group and non- transposition group are the treatment options for patients with distal humerus fractures without ulnar nerve injury. Besides, these findings need to be further verified by multi-center, double-blind, and large sample RCTs

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Using machine learning for exploratory data analysis and predictive models on large datasets

Master's thesis in Computer scienceWith the advent of the era of big data, machine learning has been widely used in many technologies and industries, which is able to get computers to learn without being explicitly programmed. As one of the fields of the supervised learning, some classical types of regression models, including the linear regression, nonlinear regression and regression trees, are discussed at first. And some representative algorithms in each category and their advantages and disadvantages are also illustrated as well. After that, the data pre-processing and resampling techniques, including data transformation, dimensionality reduction and k-fold cross-validation, are explained which can be used to improve the performance of the training model. During the implementation of machine learning algorithms, three typical models (Ordinary Linear Regression, Artificial Neural Networks and Random Forest) have been implemented by the different packages in R on the given large datasets. Apart from the model training, the regression diagnostics are conducted to explain the poorly predictive ability of the simplest ordinary linear regression model. Due to the non-deterministic feature of the artificial neural network and random forest models, several small models are built on small number of samples in the dataset to get the reasonable tuning parameters, and the optimal models are chosen by the value of RMSE and R2 among several training models. The corresponding performance of the built models are quantitatively and visually evaluated in details. The quantitative and visual results of our practical implementation show the feasibility for the large datasets under the artificial neural network and random forest algorithms. Comparing with the ordinary linear regression model (RMSE = 65556.95, R2 = 0.7327), the performance of the artificial neural network (RMSE = 36945.95, R2 = 0.9151) and random forest (RMSE = 30705.78, R2 = 0.9417) models are greatly improved, but the model training process is more complex and more time-consuming. The right choice between different models relies on the characteristics of the dataset and the goal, and also depends upon the cross-validation technique and the quantitative evaluation of the models