Search

A systematic review conducted by researchers from the School of Computer Science and Engineering at Vellore Institute of Technology in Chennai, India, explores the use of machine learning and deep learning prediction models for sepsis and neonatal sepsis. Sepsis and neonatal sepsis are significant challenges in global healthcare, as they can lead to life-threatening organ dysfunction. Early identification of sepsis is difficult, but the implementation of machine learning and deep learning technologies shows promise in predicting sepsis at an early stage and reducing mortality rates. The review analyzes various studies and concludes that prediction models can support researchers and clinicians in making better decisions and administering antibiotics earlier. [Extracted from the article]

A recent study conducted by the Department of Computer Sciences and Engineering in Secunderabad, India, has developed a new deep learning-based method called the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. This model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms to accurately segment brain tumors and predict survival outcomes. The DHA-ISSP model achieved impressive performance metrics and outperformed 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. The research highlights the potential for clinical applications of this system in personalized treatment strategies for glioma patients. [Extracted from the article]

A recent study conducted at Hunan University in Changsha, People's Republic of China, focused on predicting protein-protein interaction (PPI) binding sites, which are crucial for understanding protein functions, disease mechanisms, and drug design. The researchers proposed a new deep learning model called CLPPIS, which combines CNN and LSTM components to capture spatial and sequential features simultaneously. The model also incorporates a novel feature group and a batch-weighted loss function to address data imbalance. The study found that the CLPPIS model outperformed existing methods for predicting PPI sites. This research was supported by the National Natural Science Foundation of China. [Extracted from the article]