Ann Transl Med
Information and Network Center, The Second Xiangya Hospital, Central South University, Changsha, China.
Published: October 2022
Background: Entity relation extraction is an important task in the construction of professional knowledge graphs in the medical field. Research on entity relation extraction for academic books in the medical field has revealed that there is a great difference in the number of different entity relations, which has led to the formation of a typical unbalanced data set that is difficult to recognize but has certain research value.
Methods: In this article, we propose a new entity relation extraction method based on data augmentation. According to the distribution of individual entity relation classes in the data set, the probability of whether a text is augmented during training was calculated. In text-oriented data augmentation, different augmentation methods perform differently in different language environments. The reinforcement of learning determines which data augmentation method to use in the current language environment. This strategy was applied to the entity relation extraction of the medical professional book, , and different data augmentation methods (i.e., no data augmentation, traditional data augmentation, and reinforcement learning-based data augmentation) were compared under the same neural network model.
Results: The deep-learning model using data augmentation was better than the model without data augmentation, as data augmentation significantly improved the evaluation indicators of the relation classes with low data volumes in the unbalanced data set and slightly improved the evaluation indicators of the relation classes with sufficient features and large data volumes. Additionally, the deep-learning model using reinforcement learning-based data augmentation was superior to the deep-learning model using traditional data augmentation. We found that after the application of reinforcement learning-based data augmentation, the evaluation indicators of the multiple relation classes were much better than those to which reinforcement learning-based data augmentation had not been applied.
Conclusions: For unbalanced data sets, data augmentation can effectively improve the ability of the deep-learning model to obtain data features, and reinforcement learning-based data augmentation can further enhance this ability. Our experiments confirmed the superiority of reinforcement learning-based data augmentation.
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http://dx.doi.org/10.21037/atm-22-3991 | DOI Listing |
Acta Otolaryngol
January 2025
Department of Otorhinolaryngology, Institute of Science Tokyo, Tokyo, Japan.
Background: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.
Aim: We aimed to develop an automated diagnostic system for middle ear diseases by applying deep learning techniques to tympanic membrane images obtained during routine clinical practice.
Material And Methods: To augment the training dataset, we explored the use of generative adversarial networks (GANs) to produce high-quality synthetic tympanic images that were subsequently added to the training data.
J Clin Med
January 2025
Discipline of Physiotherapy, Faculty of Health Sciences, European University Miguel de Cervantes, C del Padre Julio Chevalier 2, 47012 Valladolid, Spain.
: Hip fractures are prevalent among the elderly and impose a significant burden on healthcare systems due to the associated high morbidity and costs. The increasing use of intramedullary nails for hip fracture fixation has inadvertently introduced risks; these implants can alter bone elasticity and create stress concentrations, leading to peri-implant fractures. The aim of this study is to investigate the outcomes of peri-implant hip fractures, evaluate the potential causes of such fractures, determine the type of treatment provided, assess the outcomes of said treatments, and establish possible improvement strategies.
View Article and Find Full Text PDFJ Clin Med
December 2024
Clinic for Masticatory Disorders and Dental Biomaterials, Center for Dental Medicine, University of Zurich, 8006 Zurich, Switzerland.
: Sinus lifting, a procedure to augment bone in the maxilla, may cause complications such as sinusitis due to impaired drainage. This study aimed to assess how sinus lifting impacts airflow in the sinus cavity, which is essential for patients undergoing dental implants. Using computational fluid dynamics (CFD), this research analyzed airflow changes after sinus floor elevation, offering insights into the aerodynamic consequences of the procedure.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Background/objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation.
Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research.
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