AI-Assisted Audit of Intensive-Care Risk Scores under Missing Laboratory Data: A Resilience-Oriented Study Protocol
DOI:
https://doi.org/10.66687/e1hf3013Keywords:
Artificial Intelligence, Machine Learning, Medical Informatics, Decision Support Systems, Clinical, Reproducibility of ResultsAbstract
Intensive-care risk scores can become unreliable when laboratory results arrive late or are missing at the moment clinicians need a prediction. The difficulty is particularly visible when a system is transferred to a new setting. The objective is to test how missing-data mechanisms affect the calibration and usefulness of AI-based deterioration alerts. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The proposed method is a retrospective, patient-level validation protocol using de-identified longitudinal ICU records, with hospital and calendar-period holdouts. Comparators will include complete-case scoring, imputation and missingness-aware prediction under matched alert budgets. Enterprise-storage sources are used to specify constraints on data availability and provenance [1], with complementary infrastructure evidence defining practical recovery safeguards [2]. These citations are not treated as clinical effectiveness evidence. The resilience component introduces missing data, unexpected interruptions and recovery dependencies as controlled disturbances. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are calibration error, sensitivity at a fixed alert rate, subgroup performance and time to actionable warning. Potential biases include patient-level leakage, changes in test-ordering practices and clinicians’ review of ambiguous outcomes. Outcomes would be assessed before and after each disturbance, including time needed to restore valid operation. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].
References
[1] Mohammed Nazir. (2022). Automated SnapMirror Migration from On-Premises Storage to Cloud Volumes ONTAP: A Replication Performance Study. Journal of Multidisciplinary Research and Integrated Sciences, 2(1), 61–72. https://jmris.com/index.php/jmris/article/view/2022V1P6
[2] Mohammed Nazir. (2022). Performance Comparison of AWS EBS, EFS and S3 for Enterprise Cloud-Storage Workloads. International Journal of Business & Computational Sciences, 2(1), 26–33. https://ijbcs.org/index.php/IJBCS/article/view/2022-01-05
[3] Azam, M. A., Ansari, I., Haque, G. M. M., & Jahid, A. (2026). Leveraging Health Information Systems and Predictive Analytics to Improve Patient Outcomes: A Data-Driven Approach. The American Journal of Medical Sciences and Pharmaceutical Research, 8(3), 45–70. https://theamericanjournals.com/index.php/tajmspr/article/view/7512
[4] Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database, version 1.4. PhysioNet. https://physionet.org/content/mimiciii/1.4/
[5] Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118. https://www.nature.com/articles/nature21056
[6] Singhal, K., Azizi, S., Tu, T., et al. (2023). Large language models encode clinical knowledge. Nature, 620, 172–180. https://www.nature.com/articles/s41586-023-06291-2
[7] McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577, 89–94. https://www.nature.com/articles/s41586-019-1799-6
[8] Begum, M. H., Talukder, S. I., Ahmed, M. J., Khan, M. F. I., & Azam, M. A. (2025). XAI-Driven Multimodal Deep Learning for Early Sepsis Prediction in ICU. Journal of Science Technology and Social Transformation, 1(1), 30–41. https://jstst.info/j/article/view/25
[9] Nahid, M. A. U., Azam, M. A., Ratul, A. S., et al. (2026). Machine Learning Meets XAI: Grad-CAM Visualization for Enhanced Lung and Colon Cancer Detection. Proceedings of the IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking. https://ieeexplore.ieee.org/document/11546191/
[10] Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. https://www.nature.com/articles/s41586-021-03819-2
[11] Liu, X., Faes, L., Kale, A. U., et al. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nature Medicine, 26, 1364–1374. https://www.nature.com/articles/s41591-020-1034-x
[12] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1705.07874
[13] Azam, M. A., Hossain, I., & Islam, M. (2024). Advancing Explainable Artificial Intelligence and Predictive Business Analytics to Strengthen Digital Transformation, Operational Resilience, and Productivity among U.S. Small and Medium-Sized Enterprises. European Journal of Management, Economics and Business, 1(1), 70–94. https://ejmeb.com/index.php/journal/article/view/323
[14] Alagappan, R. et al. (2019). Protocol-Aware Recovery for Consensus-Based Storage. USENIX ATC '19. https://www.usenix.org/conference/atc19/presentation/alagappan
[15] Patel, T. et al. (2020). Uncovering Access, Reuse, and Sharing Characteristics of I/O-Intensive Files on Large-Scale Production HPC Systems. USENIX FAST '20. https://www.usenix.org/conference/fast20/presentation/patel-hpc-systems
[16] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
[17] Li, Q. et al. (2023). More Than Capacity: Performance-oriented Evolution of Pangu in Alibaba. USENIX FAST '23. https://www.usenix.org/conference/fast23/presentation/li-qiang-deployed
[18] Maneas, S. et al. (2022). Operational Characteristics of SSDs in Enterprise Storage Systems: A Large-Scale Field Study. USENIX FAST '22. https://www.usenix.org/conference/fast22/presentation/maneas
[19] Guo, C. et al. (2017). On Calibration of Modern Neural Networks. Proceedings of Machine Learning Research, 70, 1321–1330. https://proceedings.mlr.press/v70/guo17a.html
[20] Li, T. et al. (2024). MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/li
[21] Shen, J. et al. (2023). FUSEE: A Fully Memory-Disaggregated Key-Value Store. USENIX FAST '23. https://www.usenix.org/conference/fast23/presentation/shen
[22] Pang, G. et al. (2020). Deep Learning for Anomaly Detection: A Review. arXiv:2007.02500. https://arxiv.org/abs/2007.02500
[23] Azam, M. A., Kaur, G., & Roy, K. K. (2026). Cloud-Native Data Architecture: Optimizing Enterprise Analytics Using Multi-Cloud Data Warehousing Platforms. The American Journal of Engineering and Technology, 8(9), 86–115. https://usajournal.net/index.php/tujtajet/article/view/1881
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