Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Deep Learning for Predictive Analytics in Intensive Care Units

Authors
D. B. K. Kamesh1, *, M. Srikala2, B. Vasantha3, Gandhavalla Sambasiva Rao4, N. Sreekanth5, K. V. Ranga Rao6
1Professor, Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India
2Assistant Professor, CMR Engineering College, Kandlakoya, Hyderabad, Telangana, India
3Assistant Professor, Department of IT, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India
4Professor and HOD, Department of IT, Nawab Shah Alam Khan College of Engineering & Technology, Hyderabad, Telangana, India
5Professor, Department of ECE, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India
6Professor and head, Department of CSE, Neil Geogte Institute of Technology, Hyderabad, Telangana, India
*Corresponding author. Email: kamesh.dk@mlrit.ac.in
Corresponding Author
D. B. K. Kamesh
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_33How to use a DOI?
Keywords
Deep learning; Predictive analytics; Intensive Care Unit (ICU); Sepsis prediction; Mortality prediction; Model interpretability; Healthcare analytics
Abstract

Using predictive analytics on Intensive Care Units (ICUs) has advantages because it shows an early clue of the patience’s critical triangle or deterioration. Supervised learning methods used in deep learning also seem appropriate for large data arrays and intricate structures that are why they are useful in predictive modeling. This paper seeks to discuss deep learning in the context of enhancing the performance of ICU monitoring by predicting critical events such as sepsis, cardiac arrest or mortality. By providing an overview of the current state of literature, this paper focuses on how deep learning can revolutionize ICUs. In addition, we discuss the issues of data heterogeneity, model explainability, and real-time integration of insights, and outline ideas for further research to overcome these shortcomings.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_33How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - D. B. K. Kamesh
AU  - M. Srikala
AU  - B. Vasantha
AU  - Gandhavalla Sambasiva Rao
AU  - N. Sreekanth
AU  - K. V. Ranga Rao
PY  - 2025
DA  - 2025/05/23
TI  - Deep Learning for Predictive Analytics in Intensive Care Units
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 381
EP  - 390
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_33
DO  - 10.2991/978-94-6463-718-2_33
ID  - Kamesh2025
ER  -