Health Information Science (eBook, PDF)

13th International Conference, HIS 2024, Hong Kong, China, December 8-10, 2024, Proceedings
Redaktion: Siuly, Siuly; Zhou, Rui; Li, Xiaofan; Xing, Chunxiao

• Format: PDF

Produktbeschreibung

This book LNCS 15336 constitutes the refereed proceedings of the 13th International Conference on Health Information Science, HIS 2024, held in Hong Kong, China, during December 8-10, 2024.

The 18 full papers and 11 short papers were carefully reviewed and selected from 59 submissions. The scope of the conference includes:

(1) medical/health/biomedicine information resources, such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain;

(2) data management, data mining, and knowledge discovery, all of which play a key role in decision-making, management of public health, examination of standards, privacy and security issues;

(3) computer visualization and artificial intelligence for computer-aided diagnosis;

(4) development of new architectures and applications for health information systems.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 340
• Erscheinungstermin: 15. Mai 2025
• Englisch
• ISBN-13: 9789819655977
• Artikelnr.: 74230346

Inhaltsangabe

Artificial Intelligence in Enhancing Electronic Health Record Systems: A Comprehensive Survey.- Complex Query Optimization in Sepsis Data Using Learned Index with Data Transformation.- A Novel Feature Selection Framework Based on Large Language Models.- LTGAN: Multi-label Time-series GAN with Constraints for Electronic Health Records Generation.- Multi-condition Join Query Optimization For Disease-specific Data Exploration Based On Data Lake.- Multi-modal Medical Data Management Platform Based on Data Lake.- Comparative Study of Machine Learning Algorithms for IoT Cyber Threat Detection in Healthcare Information Systems.- Myocardial Band Transformer Network for Detecting Myocardial Ischemia in 2D Echocardiography.- Deep Learning-Enabled Electronic Health Records for Accurate Diagnosis of Cardiovascular Diseases: A Retrospective, Prospective, Diagnostic Study.- V-GnNet: Voxel and Graph Node Based Network for Continuously Consistent Artery and Vein Classification in Non-contrast CT Images.- Multilabel Classification for Cancer Subtypes.- TG.Net A deep learning model combining attention mechanism for effective prediction of multi stages of Chronic Kidney Disease.- A Deep Transfer Learning Approach for Predicting Diabetes Complications Using Genomic Data.- Using machine learning to predict the risk severity of late effects of a childhood rhabdomyosarcoma survivors.- Predicting C-section Outcomes among Bangladeshi Women: A Comparative Study of Machine Learning Techniques.- Exploring Relations between Depression and Cognitive Impairment.- Investigating Roles of Immune Functions in Depression by Using Knowledge Graph Approach.- EEG Sleep Classification Based on Fourier-Bessel Technique Coupled with LS-SVM.- An AI driven framework for EEG based-BCI technology.- Perceptions of Academic Performance and Mental Health Among Pharmacy Students Across Different Learning Modalities.- Epilepsy Detection from Weighted EEG Graph (WEG) using Novel Feature- Normalized Weighted Forgotten Topological Index (NWFT-index).- Extended Knowledge Graphs of Depression.- IBDR-Net: A Computerized Framework for Irregular Brain Signal Data Recognition.- Physical Human Activity Recognition Based on Spectral Graph Wavelet Transforms Integrated with Machine Learning Model.- Robust Approach for Human Activity Recognition Using Decomposition Technique Based Machine Learning Models.- The Impact of COVID-19 and the Ukrainian war on the share market index for the Australian Financial sector.- The Impact of COVID-19 on Global GMAT Test-Taking Patterns: A Statistical Analysis.- A Geriatric Disease Medical Consultation System Based on Multi-Agent Architecture and Knowledge Graph.- Impact of social media on heart health-related behaviours in health promotion interventions: sentiment analysis of tweets linked to the American Heart Month campaigns.