Journal of Information Technology Management

Journal of Information Technology Management

A Smart Healthcare Management System for Predicting ICU Length of Stay Using Machine Learning and MIMIC-IV

Document Type : Research Paper

Authors
Danish Ather 1
Dharm Raj 2
Suhail Javed Quraishi 3
Sonal Pathak 3
Manik Rakhra 4
Kunchanapaalli Rama Krishna 5
1 Professor, Department of Computer Science and Engineering, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent 100084, Uzbekistan.
2 Assistant Professor, Department of Computer Science and Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh 201306, India.
3 Assistant Professor, Manav Rachna International Institute of Research and Studies, Sector 43, Faridabad, Haryana 121004, India.
4 Assistant Professor, School of Computer Science and Engineering, Lovely Professional University, Phagwara, India.
5 Professor, Department of Computer Science and Information Technology, K L Deemed to be University, Vaddeswaram 522502, Andhra Pradesh, India.
10.22059/jitm.2026.108919
Abstract
Stays in ICUs are expensive to accommodate and difficult to forecast, yet even a simple forecast can be helpful, as it enables ICUs to allocate beds and staff and schedule transfer times. We address a particular problem of this kind: how much of the length of stay can be determined based on the first day of hospitalization? Our dataset is the publicly available MIMIC-IV Clinical Database Demo, from which we use 140 hospitalization records, retain the 117 that lasted at least one day, and derive 47 features from the first 24 hours of data on vital signs, medication and fluid intake, urine volume, and bedside procedures. Both formulations are addressed for the same set of patients. Exact forecasting of duration turned out to be rather complicated in our case: despite selecting the best-performing regression technique (random forest), the resulting mean absolute error was 2.85 days, barely outperforming the mean baseline predictor and producing a negative value for the coefficient of determination due to a few very long stays contributing disproportionately to the squared error. Yet, what has been accomplished is the ranking, where the Spearman rank correlation.
Keywords
ICU Length of Stay
Machine Learning
Electronic Health Records
MIMIC-IV
Predictive Modelling
Critical Care
Agustsson, E., & Timofte, R. (2017). NTIRE 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 126–135).
Alvarez, G., & Li, S. (2006). Some basic cryptographic requirements for chaos-based cryptosystems. International Journal of Bifurcation and Chaos, 16(8), 2129–2151.
Amrit, P., et al. (2023). EmbedR-Net: Using CNN for secure eHealth systems. IEEE Transactions on Consumer Electronics, 69(4), 1017–1022.
Amutha, R. (2026). Chaotic image encryption using circular shift and modular arithmetic. Journal of Discrete Mathematical Sciences and Cryptography, 29(1), 215–228. https://doi.org/10.47974/JDMSC-2411
Aumasson, J.-P., Neves, S., Wilcox-O'Hearn, Z., & Winnerlein, C. (2013). BLAKE2: Simpler, smaller, fast as MD5. In Proceedings of the 11th International Conference on Applied Cryptography and Network Security (ACNS) (pp. 119–135).
Barker, E. (2020). Recommendation for key management—Part 1: General (NIST Special Publication 800-57 Part 1 Rev. 5). National Institute of Standards and Technology.
Bourekouche, H., Belkacem, S., & Messaoudi, N. (2025). Design and analysis of a PRNG based on the logistic-sine system. Journal of Discrete Mathematical Sciences and Cryptography, 28(7), 2645–2670.
Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 21–30.
Daugman, J. (2006). Probing the uniqueness and randomness of IrisCodes. Proceedings of the IEEE, 94(11), 1927–1935.
Fridrich, J. (1998). Symmetric ciphers based on two-dimensional chaotic maps. International Journal of Bifurcation and Chaos, 8(6), 1259–1284.
Huang, X., Ye, Y., Dai, S., & Zhang, Y. (2023). Image encryption scheme using chaotic map and pixel-level diffusion with S-box substitution. Mathematics, 11(4), Article 866. https://doi.org/10.3390/math11040866
ISO/IEC. (2022). Information security, cybersecurity and privacy protection—Information security management systems—Requirements (ISO/IEC 27001:2022). International Organization for Standardization.
Jain, A. K., Nandakumar, K., & Nagar, A. (2008). Biometric template security. EURASIP Journal on Advances in Signal Processing, 2008, Article 579416.
Jyotsna, L., & Kumar, L. P. (2026). Enhanced image security through block permutation. Journal of Discrete Mathematical Sciences and Cryptography, 29(3), 1313–1333.
Krawczyk, H., & Eronen, P. (2010). HMAC-based extract-and-expand key derivation function (HKDF) (RFC 5869). Internet Engineering Task Force.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
Rohhila, S., & Singh, A. K. (2024). Deep learning-based encryption for secure digital images: A survey. Computers & Electrical Engineering, 116, Article 109236.
Rohhila, S., Singh, K. N., Singh, A. K., & Gupta, B. B. (2025). DeepKey-TPE: Using deep learning to generate key for image security with continued usability through thumbnail-preserving encryption. IEEE Transactions on Consumer Electronics. Advance online publication. https://doi.org/10.1109/TCE.2025.3605213
Singh, A. K., & Agrawal, A. K. (2026). HybridCrypt-AE: A deep learning–augmented hyperchaotic image encryption scheme with dual-feedback diffusion and SHA-512 key generation. Journal of Discrete Mathematical Sciences and Cryptography.
Stallings, W. (2023). Cryptography and network security (8th ed.). Pearson.
Wang, X., & Liu, C. (2017). A novel and effective image encryption algorithm based on chaos and DNA encoding. Multimedia Tools and Applications, 76(5), 6229–6245. https://doi.org/10.1007/s11042-016-3311-8
Journal of Information Technology Management
Volume 18, Issue 4
2026
Pages 134-149