Open Access
| Issue |
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 10 | |
| Section | Artificial Intelligence & Machine Learning | |
| DOI | https://doi.org/10.1051/itmconf/20257601001 | |
| Published online | 25 March 2025 | |
- Carrasco Ramírez, J. (2024). AI-Driven Predictive Analytics, Healthcare Outcomes, Cost Efficiency, and Hospital Resource Management. AIJMR. Retrieved from aijmr.com [Google Scholar]
- Shickel, B., Loftus, T. J., Ruppert, M., Upchurch, G R., & Ozragat-Baslanti, T. (2023). Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks. Scientific Reports, 13(1), 1234. [Google Scholar]
- Wang, J., Luo, J., Ye, M., Wang, X., Zhong, Y., Chang, A., Huang, G., Yin, Z., Xiao, C., Sun, J., & Ma, F. (2024). Recent Advances in Predictive Modeling with Electronic Health Records. arXiv preprint arXiv:2402.01077. [Google Scholar]
- Hennebelle, A., Materwala, H., & Ismail, L. (2023). HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System. arXiv preprint arXiv:2301.10450. [Google Scholar]
- Ayesh, A. (2024). User-Centric AI Analytics for Chronic Health Conditions Management. arXiv preprint arXiv:2402.01652. [Google Scholar]
- Lim, A., Singh, A., Chiam, J., Eckert, C., Kumar, V., Ahmad, M. A., & Teredesai, A. (2021). Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management. arXiv preprint arXiv:2104.07820. [Google Scholar]
- Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021). An empirical characterization of fair machine learning for clinical risk prediction. Journal of Biomedical Informatics, 113, 103621. [Google Scholar]
- Shah, N. H., Milstein, A., & Bagley, S. C. (2019). Making Machine Learning Models Clinically Useful. JAMA, 322(14), 1351–1352. [Google Scholar]
- Jung, K., Kashyap, S., Avati, A., Harman, S., Shaw, H., & Shah, N. H. (2021). A framework for making predictive models useful in practice. Journal of the American Medical Informatics Association, 28(6), 1149–1158. [Google Scholar]
- Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604. [Google Scholar]
- Ozrazgat-Baslanti, T., Loftus, T. J., Mohandas, R., Wu, Q., Brakenridge, S., & Bihorac, A. (2022). Clinical Trajectories of Acute Kidney Injury in Surgical Sepsis: A Prospective Observational Study. Annals of Surgery, 275(6), e626–e633. [Google Scholar]
- Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., & Bihorac, A. (2020). Artificial Intelligence and Surgical Decision-making. JAMA Surgery, 155(2), 148–158. [Google Scholar]
- Li, H., & Sheu, P. C.-Y. (2022). A scalable association rule learning and recommendation algorithm for large-scale microarray datasets. Journal of Big Data, 9(1), 28. [Google Scholar]
- Wang, C. C. N., Jin, J., Chang, J.-G., Hayakawa, M., Kitazawa, A., & Sheu, P. C.-Y. (2020). Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization. BMC Medical Informatics and Decision Making, 20(1), 212. [Google Scholar]
- Chou, C.-H., Sheu, P., Hayakawa, M., & Kitazawa, A. (2020). Querying large graphs in biomedicine with colored graphs and decomposition. Journal of Biomedical Informatics, 107, 103481. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.