Article
Automation & Control Systems
Yiteng Pan, Fazhi He, Xiaohu Yan, Haoran Li
Summary: Motivated by the success of deep learning techniques, this paper introduces two typical methods of deep learning models developed for recommender systems: user-oriented autoencoder and item-oriented autoencoder. Studies show that the IAE model performs better in rating prediction tasks, while the UAE model performs better in top-N recommendation tasks. The authors propose a new SHAE method that combines the features learned by IAE and UAE models, achieving efficient recommendations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Lingyun Song, Haodong Li, Yacong Tan, Zhanhuai Li, Xuequn Shang
Summary: The assessment of Enterprise Credit Risk (ECR) is crucial for investment decisions and financial regulation. Researchers propose a graph learning based method to enhance enterprise representation learning using the neighbor structure of enterprise graphs, aiming to alleviate indicator deficiency for small and medium-sized enterprises. They introduce a Multi-Structure Cascaded Graph Neural Network framework (MS-CGNN) that leverages enterprise graph structures of different granularity, achieving state-of-the-art performance on real-world ECR datasets.
Article
Computer Science, Artificial Intelligence
Haoran Zhao, Xin Sun, Junyu Dong, Zihe Dong, Qiong Li
Summary: A curriculum learning knowledge distillation framework is proposed in this study, using instance-level sequence learning to gradually guide the student network and bridge the feature representation gap between the teacher and student networks step by step. Extensive experiments show that the framework performs well on several datasets and achieves the best performance with fewer iterations.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Business
Hui Yuan, Weiwei Deng
Summary: This study proposes an advanced doctor recommendation method that leverages a health knowledge graph to address data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method outperforms diverse baseline methods and offers practical solutions for online platforms facing information overload and transparency issues.
Review
Medicine, General & Internal
David Rodriguez, Jhon Diego Martinez-Alvarado, Rebeca Garcia-Toto, Tania Itzel Genel-Rey
Summary: This study evaluated the teaching of evidence-based medicine (EBM) in medical curriculums in Mexico and found that EBM teaching is limited and of minimal value. A comprehensive review of curriculums is needed to incorporate EBM and improve medical education and public health.
BMJ EVIDENCE-BASED MEDICINE
(2023)
Article
Computer Science, Information Systems
Diana Olivia, Cynthia Amrutha, Ashalatha Nayak, Mamatha Balachandra, Arnav Saxena
Summary: This research proposes a multi-methodological approach that integrates a prediction model and an optimization model to control the mortality rate at the Mass Casualty Incident. The experiments on the MIMIC-II dataset show that the proposed approach supports reduction in mortality length and outperforms existing literature in terms of performance parameters such as mortality length and Queue length.
Article
Computer Science, Artificial Intelligence
Chao Suo, Tianxin Zhou, Kai Hu, Yuan Zhang, Xieping Gao
Summary: Efficient and accurate medical image segmentation is crucial for pathological evaluation and disease diagnosis. This paper proposes a cross-level collaborative context-aware framework (C3-Net) to address the semantic asymmetry and global semantic dilution problems in the U-shaped encoder-decoder structure. The proposed method achieves state-of-the-art performance on multiple datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Chemistry, Multidisciplinary
Dehai Zhang, Anquan Ren, Jiashu Liang, Qing Liu, Haoxing Wang, Yu Ma
Summary: This paper focuses on the automatic generation of medical reports from chest X-ray images. By constructing associations based on a knowledge graph and using a graph neural network, disease situational representations with prior knowledge are generated, and radiology reports are generated using self-supervised learning. Experimental results demonstrate that this method outperforms existing methods in performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
Summary: In the field of medical image analysis, researchers are seeking external information beyond current medical datasets to address the small size issue. They integrate domain knowledge from medical doctors into deep learning models, mimicking diagnostic patterns and features of doctors, and applying them to tasks such as disease diagnosis and organ segmentation.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Cell Biology
Joshua M. Wang, Runyu Hong, Elizabeth G. Demicco, Jimin Tan, Rossana Lazcano, Andre L. Moreira, Yize Li, Anna Calinawan, Narges Razavian, Tobias Schraink, Michael A. Gillette, Gilbert S. Omenn, Eunkyung An, Henry Rodriguez, Aristotelis Tsirigos, Kelly Ruggles, Li Ding, Ana I. Robles, D. R. Mani, Karin D. Rodland, Alexander J. Lazar, Wenke Liu, David Fenyo
Summary: This study introduces a pioneering approach that combines pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. By utilizing a large dataset and advanced models, the researchers were able to accurately replicate distinctions made by human pathologists and discover previously unused predictive morphologies in a clinical setting.
CELL REPORTS MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Batuhan Bardak, Mehmet Tan
Summary: The study focuses on improving mortality and LOS predictions using medical entities and a convolution-based multimodal architecture. Results show that the proposed method outperforms baseline models by around 3% in mortality prediction performance and around 2.5% in LOS prediction performance.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Engineering, Environmental
Sang-Soo Baek, Eun-Young Jung, JongCheol Pyo, Yakov Pachepsky, Heejong Son, Kyung Hwa Cho
Summary: Harmful algal blooms have become a global issue, and model development is an alternative approach for understanding and managing them. Traditional modeling methods have limitations in simulating phytoplankton and zooplankton, while deep learning models show potential for simulating harmful algal blooms.
Article
Biochemical Research Methods
Jana Schor, Patrick Scheibe, Matthias Bernt, Wibke Busch, Chih Lai, Joerg Hackermueller
Summary: Many chemicals in the environment pose risks if not assessed properly; limitations in computational approaches due to lack of labeled training data is a major challenge.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
Summary: This paper reviews the recent studies on applying deep learning methods in medical image analysis, emphasizing the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in this field. It also discusses major technical challenges and suggests possible solutions for future research efforts.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Ozan Ozyegen, Devika Kabe, Mucahit Cevik
Summary: This paper demonstrates how different text highlighting techniques can alleviate the workload of medical professionals and improve the quality of online medical services. The numerical study shows that the neural network approach is successful in highlighting medically-relevant terms.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
