A Temporal Patient Risk Timeline Framework for Longitudinal Clinical Deterioration Surveillance Using Hospital Data Warehouse and Machine Learning
DOI:
https://doi.org/10.63158/journalisi.v8i4.1680Keywords:
clinical deterioration, ICU transfer, temporal monitoring, XGBoost, retrospective dashboard prototypeAbstract
Clinical deterioration among hospitalized patients remains a major challenge in inpatient care management. Conventional deterioration monitoring approaches frequently rely on isolated physiological observations and static threshold-based scoring systems, which may inadequately represent the longitudinal nature of patient instability. This study aimed to develop and evaluate a retrospective temporal patient risk timeline framework for clinical deterioration surveillance using hospital data warehouse and machine learning. A retrospective longitudinal deterioration surveillance framework was developed using hourly inpatient observations derived from hospital operational and clinical data sources. A temporal XGBoost model was developed to predict ICU transfer within the subsequent 24-hour observation window under severe class imbalance conditions. To evaluate the additional value of temporal feature engineering, baseline non-temporal models including logistic regression, random forest, and non-temporal XGBoost were also developed using static observation-level variables. Operational feasibility was retrospectively demonstrated through a dashboard prototype integrating deterioration prioritization, patient timeline visualization, and longitudinal surveillance. The final dataset consisted of 20,181 inpatient admission episodes represented by 2,233,143 hourly observations. ICU transfer occurred in 712 admissions. Because the prediction task was formulated as hourly deterioration surveillance, ICU transfer labels represented only 0.65% of all hourly observations, indicating severe class imbalance. The temporal XGBoost model achieved an AUROC of 0.606 and an AUPRC of 0.009. While predictive discrimination was modest, the framework enabled continuous longitudinal representation of patient deterioration and retrospective surveillance of clinical instability. The findings suggest that temporal patient representation may provide operational and interpretative value beyond static snapshot-based deterioration monitoring approaches.
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[1] S. Anagi et al., “Delayed Admission to the Intensive Care Unit Is Associated With Increased Mortality Risk in Critically Ill Patients: A Prospective Cohort Study,” J. Adv. Nurs., 2026, doi: 10.1111/jan.70493.
[2] R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, “An overview of clinical decision support systems: benefits, risks, and strategies for success,” Dec. 01, 2020, Nature Research. doi: 10.1038/s41746-020-0221-y.
[3] C. Hincapié-Osorno et al., “Validation of MEWS, NEWS, NEWS-2 and qSOFA for different infection foci at the emergency department, the acutelines cohort,” European Journal of Clinical Microbiology and Infectious Diseases, vol. 43, no. 12, pp. 2441–2452, Dec. 2024, doi: 10.1007/s10096-024-04961-1.
[4] S. Gerry et al., “Early warning scores for detecting deterioration in adult hospital patients: Systematic review and critical appraisal of methodology,” May 20, 2020, BMJ Publishing Group. doi: 10.1136/bmj.m1501.
[5] A. Shahu, W. Xia, Y. Wei, and D. Malinsky, “Estimating Effects of Longitudinal Modified Treatment Policies (LMTPs) on Rates of Change in Health Outcomes,” Stat. Med., vol. 45, no. 13–14, Jun. 2026, doi: 10.1002/sim.70604.
[6] S. Adegoke, “Temporal Pattern Recognition and Unsupervised Anomaly Detection for Early Warning of Disease Progression in Longitudinal Health Records,” International Journal of Computer Applications Technology and Research, Jan. 2026, doi: 10.7753/IJCATR1212.1027.
[7] J. Wang et al., “Recent Advances in Predictive Modeling with Electronic Health Records,” 2024.
[8] H. Huang, W. Lyu, M. M. Hasan, and S. H. Houser, “Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study,” J. Med. Internet Res., vol. 27, 2025, doi: 10.2196/76126.
[9] A. Khumaidi, “Application of Machine Learning to Determine the Factors Affecting Deterioration in Patients with Chronic Kidney Disease,” vol. 12, no. 4, 2022.
[10] J. C. Ginestra et al., “Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock,” Crit. Care Med., vol. 47, no. 11, pp. 1477–1484, Nov. 2019, doi: 10.1097/CCM.0000000000003803.
[11] S. Muralitharan et al., “Machine learning–Based early warning systems for clinical deterioration: Systematic scoping review,” J. Med. Internet Res., vol. 23, no. 2, Feb. 2021, doi: 10.2196/25187.
[12] A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” New England Journal of Medicine, vol. 380, no. 14, pp. 1347–1358, Apr. 2019, doi: 10.1056/nejmra1814259.
[13] S. M. Lauritsen et al., “Explainable artificial intelligence model to predict acute critical illness from electronic health records,” Nat. Commun., vol. 11, no. 1, Dec. 2020, doi: 10.1038/s41467-020-17431-x.
[14] B. I. Maijamaa, S. Ahmad, A. Musa, A. Ishaq, and A. Ayuba, “Ensemble-Based Imputation for Handling Missing Values in Healthcare Datasets: A Comparative Study of Machine Learning Models,” in The 6th International Electronic Conference on Applied Sciences, Basel Switzerland: MDPI, Feb. 2026, p. 21. doi: 10.3390/engproc2026124021.
[15] A. A. Wani and F. Abeer, “Comprehensive analysis of missing data imputation in clinical time-series: challenges, risks, and practical solutions,” PeerJ Comput. Sci., vol. 12, 2026, doi: 10.7717/peerj-cs.3848.
[16] Y. Raita, T. Goto, M. K. Faridi, D. F. M. Brown, C. A. Camargo, and K. Hasegawa, “Emergency department triage prediction of clinical outcomes using machine learning models,” Crit. Care, vol. 23, no. 1, Feb. 2019, doi: 10.1186/s13054-019-2351-7.
[17] J. Futoma, S. Hariharan, and K. Heller, “Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier,” Jun. 2017, [Online]. Available: http://arxiv.org/abs/1706.04152
[18] T. J. Moss et al., “Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation∗,” Crit. Care Med., vol. 44, no. 9, pp. 1639–1648, Sep. 2016, doi: 10.1097/CCM.0000000000001738.
[19] H. Harutyunyan, H. Khachatrian, D. C. Kale, G. Ver Steeg, and A. Galstyan, “Multitask learning and benchmarking with clinical time series data,” Aug. 2019, doi: 10.1038/s41597-019-0103-9.
[20] I. Vagliano, O. Lvova, and M. C. Schut, “Interpretable and continuous prediction of acute kidney injury in the intensive care,” in Public Health and Informatics: Proceedings of MIE 2021, IOS Press, 2021, pp. 103–107. doi: 10.3233/SHTI210129.
[21] D. W. Bates et al., “The potential of artificial intelligence to improve patient safety: a scoping review,” Dec. 01, 2021, Nature Research. doi: 10.1038/s41746-021-00423-6.
[22] T. A. K. Manne, M. Jindal, S. S. Saini, A. M. Sivagnanam, G. Surianarayanan, and P. C. Jakku, “Real-Time AI–Driven Decision Intelligence For Enterprise Healthcare Operations: Cloud-Native Architecture, Workflow Integration, And Measured Operational Outcomes,” International Journal of Drug Delivery Technology, vol. 16, no. 12, pp. 1039–1051, 2026, doi: 10.25258/ijddt.16.12s.115.
[23] Bolatito khadijat Aremu and Olagunju Omotola Peggy, “Advanced machine learning-driven business analytics for real-time health risk stratification and cost prediction models,” World Journal of Advanced Research and Reviews, vol. 26, no. 2, pp. 150–167, May 2025, doi: 10.30574/wjarr.2025.26.2.1583.
[24] S. Lyu, S. Craig, G. O’Reilly, and D. Taniar, “The development and use of data warehousing in clinical settings: a scoping review,” 2025, Frontiers Media SA. doi: 10.3389/fdgth.2025.1599514.
[25] A. Al-Juhani et al., “Advances in Data-Driven Early Warning Systems for Sepsis Recognition and Intervention in Emergency Care: A Systematic Review of Diagnostic Performance and Clinical Outcomes,” Cureus, Aug. 2025, doi: 10.7759/cureus.89882.
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