TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Deep learning-enhanced decision support systems for optimized health informatics solutions

, * , , , , ,

* Corresponding author · click or hover a name for details

pp. 1265–1275Vol. 46Issue 4-BMay 2025DOI: 10.47974/JIOS-1988XML
Received:
09 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1988
Pages:
1265–1275

Abstract

The integration of Deep learning-enhanced choice back frameworks (DSS) in wellbeing informatics has illustrated noteworthy potential to convert healthcare conveyance. This paper investigates the execution and effect of these frameworks over different spaces, counting demonstrative exactness, prescient analytics, operational efficiencies, and personalized persistent care. Utilizing progressed Deep learning models such as convolutional neural systems (CNNs) and long short-term memory systems (LSTMs), these frameworks have appeared a stamped enhancement in demonstrative exactness with diminishments in blunder rates and speedier demonstrative times. Prescient analytics encouraged by these models have upgraded quiet result determining and asset administration, contributing to more productive clinic operations. Moreover, operational efficiencies have been altogether boosted through robotized forms, decreasing the authoritative burden on healthcare suppliers. Personalized quiet care has too seen eminent advancements, with custom fitted treatment plans expanding treatment viability and quiet fulfilment.

Keywords

Subject Classifications

68Q07

References

[1] M. Havaei, N. Guizard, H. Larochelle, and P.-M. Jodoin, “Deep learning trends for focal brain pathology segmentation in MRI,” in Machine Learning for Health Informatics. Cham, Switzerland: Springer, pp. 125–148 (2016).
[2] H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1153–1159 (May 2016).
[3] J. Andreu-Perez, C. C. Y. Poon, R. D. Merrifield, S. T. C. Wong, and G.-Z. Yang, “Big data for health,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 4, pp. 1193–1208 (Jul. 2015).
[4] P.-Y. Wu, C.-W. Cheng, C. D. Kaddi, J. Venugopalan, R. Hoffman, and M. D. Wang, “Omic and electronic health record big data analytics for precision medicine,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 2, pp. 263–273 (Feb. 2017).
[5] Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision to enhance cancer diagnosis and classification,” in Proceedings of the International Conference on Machine Learning (ICML) (2013).
[6] B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey, “Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning,” Nature Biotechnology, vol. 33, no. 8, pp. 831–838 (2015).
[7] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444 (May 2015).
[8] Belard, T. Buchman, J. Forsberg, et al., “Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care,” Journal of Clinical Monitoring and Computing, vol. 31, no. 2, pp. 261–271 (2017).
[9] R. Golchha, P. Khobragade and A. Talekar, “Design of an Efficient Model for Health Status Prediction Using LSTM, Transformer, and Bayesian Neural Networks,” 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), Nagpur, India, pp. 1-5 (2024), doi: 10.1109/ICICET59348.2024.10616353.
[10] Wulff, S. Montag, M. Marschollek, and T. Jack, “Clinical decision-support systems for detection of systemic inflammatory response syndrome, sepsis, and septic shock in critically ill patients: A systematic review,” Methods of Information in Medicine, vol. 58, pp. 43–e57 (2019).
[11] M. E. Nolan, R. Cartin-Ceba, P. Moreno-Franco, B. Pickering, and V. Herasevich, “A multisite survey study of EMR review habits, information needs, and display preferences among medical ICU clinicians evaluating new patients,” Applied Clinical Informatics, vol. 8, no. 4, pp. 1197–1207 (2017).
[12] T. A. Lasko, D. A. Owens, D. Fabbri, J. P. Wanderer, J. Z. Genkins, and L. L. Novak, “User-centered clinical display design issues for inpatient providers,” Applied Clinical Informatics, vol. 11, no. 5, pp. 700–709 (2020).
[13] X. Zhang, X. Li, J. An, L. Gao, B. Hou, and C. Li, “Natural language description of remote sensing images based on deep learning,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, pp. 4798–4801 (2017).

Views: 206Downloads: 106Citations: 0