A5101793840- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare
- Technology and Security Systems
- Big Data Technologies and Applications
- Advanced Computing and Algorithms
- ECG Monitoring and Analysis
- Smart Grid and Power Systems
- Advanced Computational Techniques and Applications
- Topic Modeling
- Image and Signal Denoising Methods
- Safety and Risk Management
- Biomedical Text Mining and Ontologies
- Time Series Analysis and Forecasting
- Advanced Image Processing Techniques
- Advanced Image Fusion Techniques
- Service-Oriented Architecture and Web Services
- Web Applications and Data Management
- Risk and Safety Analysis
- Data Mining Algorithms and Applications
- Electrical Fault Detection and Protection
Nantong University
2009-2023
Affiliated Hospital of Nantong University
2009-2023
In the medical field, electronic records contain a large amount of textual information, and unstructured nature this information makes data extraction analysis challenging. Therefore, automatic entity from has become significant issue in healthcare domain.To address problem, paper proposes deep learning-based model called Entity-BERT. The aims to leverage powerful feature capabilities learning pre-training language representation BERT(Bidirectional Encoder Representations Transformers),...
Medical Image Super-Resolution plays a pivotal role in enhancing diagnostic accuracy. Transformer-based methods, such as Restoration Using Swin Transformer (SwinIR) and transformer for fast Magnetic Resonance Imaging (SwinMR), have shown prowess this area but also exhibit limitations. Specifically, LayerNorm channel normalization diminishes high-frequency detail, while the Multilayer Perceptron prioritizes global information over local information. Moreover, low-resolution inputs contain...
Aiming the problems that clinical data of different patients is difficult for reasonable representation and time interval between medical events different, which lead to difficulty prediction, a prediction model based on long short-term memory (LSTM) network optimized by fruit fly optimization algorithm in health series proposed. First, FastText method used represent interpretable vector events, can extract concept relationship rich information more effectively. Then, considering strong...
Abstract Existing systems for diagnosing heart diseases are time consuming, expensive, and error prone. Aiming at this, a detection algorithm factors inducing based on particle swarm optimisation-support vector machine (PSO-SVM) optimised by association rules (ARs) was proposed. Firstly, AR used to select features from disease data set so as train feature sets. Then, PSO-SVM classify training testing sets, then the were analysed. Finally, effectiveness reliability of proposed verified...