Artificial Intelligence and mortality prediction in ICU

Artificial intelligence in the ICU transforms the first 24 hours from a period of Risk into an opportunity for Outcome-oriented intervention
Predictive analytics combined with first-day ICU data has the potential to transform mortality prediction from static, retrospective assessment into dynamic, real-time decision support. For safe and effective deployment.
AI models must follow internationally recognized guidelines

1. Guidelines of Artifical Intelligence & Predictive Analytics in Early Mortality Prediction Models Using the First Day ICU Data Critical Care Medicine Cairo University 2025 Ahmed Samir Elsawy, MD

2. Introduction Role of First-Day ICU Data in Mortality Prediction Artificial Intelligence Guidelines Traditional ICU Mortality Prediction Tools Introduction to Predictive Analytics Artificial Intelligence in Mortality Prediction Sources of First-Day ICU Data Guidelines for AI Model Development Case Studies & Examples Agenda

3. Introduction ‘Early ICU mortality prediction is not only focused on estimating mortality risk . it is about saving lives through : Early mortality prediction allows ICUs to benchmark outcomes and identify system gaps. Provides feedback for continuous improvement in care delivery. 7. Role of Biomarkers and AI 1. Timely and Targeted Interventions 2. Optimizing Resource Allocation 3. Communication and Ethical Decision-Making 4. Enhancing Clinical Research and Trial Design 5. Transition from Reactive to Proactive Critical Care 6. Quality Improvement

4. 1. Critical Prognostic Window The first 24 hours in the ICU is often referred to as the "golden window" for prognostication. 2. Facilitating Early Risk Stratification 3. Capturing Baseline Disease Severity 4. Standardization for Prediction Models 5. Enabling Predictive Analytics and AI 6. Enable ethical decision-making Mesinovic, M., et al (2025). Role of First-Day ICU Data in Mortality Prediction

5. Artificial Intelligence Guidelines Fairness Transparency and Explainability Safety and Effectiveness Privacy and Data Protection Accountability Sustainability and Updatability

6. 1. Fairness AI models must be assessed for bias across diverse patient subgroups such as gender, age, and c ultural identity . Balanced performance is essential to avoid discrimination in life-critical decisions. WHO. (2024) Artificial Intelligence Guidelines

7. Healthcare professionals must interpret AI outputs. SHAP ( SHapley Additive exPlanations ) and LIME (Local Interpretable Model-agnostic Explanations) are recommended to explain predictions. 2. Transparency and Explainability Ribeiro, M. T., et al. (2024). Artificial Intelligence Guidelines

8. AI systems must be clinically validated on real-world ICU datasets before deployment to prevent harmful decisions. 3. Safety and Effectiveness European Commission. (2024). Artificial Intelligence Guidelines

9. Use de-identified patient data in compliance with GDPR ( General Data Protection Regulation) or local laws. Consent and secure storage are essential. 4. Privacy And Data Protection EU. (2024). GDPR Artificial Intelligence Guidelines

10. Human oversight is mandatory. Developers must document the model lifecycle from data sourcing to deployment. 5. Accountability OECD. (2025) Artificial Intelligence Guidelines

11. Models should be retrainable with new data and adaptable to various ICU environments. 6. Sustainability and Updatability Lin, Y., et al. (2025). Artificial Intelligence Guidelines

12. APACHE (Acute Physiology and Chronic Health Evaluation) Versions: APACHE II, III, IV SAPS (Simplified Acute Physiology Score) Versions: SAPS II, SAPS III SOFA (Sequential Organ Failure Assessment) MPM (Mortality Probability Models) Versions: MPM II, MPM III Glasgow Coma Scale (GCS) ICU-specific Trauma Scores International Journal for Research Trends and Innovation (www.ijrti.org) 2025 IJRTI Traditional ICU Mortality Prediction Tools

13. Static Nature Delayed Prediction Time-consuming and error-prone in busy ICU Limited Variables Inflexibility Across Populations No Integration with Real-Time Data Streams Not Designed for Individual Prognosis Outdated Models https://arxiv.org/pdf/2505.12344 Limitations of Traditional ICU Mortality Prediction Tools

14. Dynamic risk assessment in real time Integration of diverse data sources Higher predictive accuracy than traditional tools Personalized patient-specific predictions Early detection of clinical deterioration Automation of calculations Continuous model learning and improvement Adaptability to new biomarkers and monitoring technologies Al and Predictive Analytics in Critical Care: Bridging Gaps in Mortality Prediction Journal of Clinical Medicine,2025

15. Definition: Predictive Analytics in the ICU refers to using historical and real-time patient data, combined with statistical models, machine learning, or AI techniques, to predict future clinical events or outcomes before they happen. Goal in ICU Anticipate complications, deterioration, or death early enough to allow intervention. Support personalized care planning. Predictive Analytics Enhancing Critical Care Outcomes StatPearls (2025)

16. How Predictive analytics Work: Collect Data: Clinical, demographic, lab, vital signs, etc. Analyze Patterns: Use statistical and AI tools to find trends or risk factors. Build a Model: Train algorithms to recognize patterns that precede an event (e.g., death, organ failure). Make Predictions: Apply the model to new patients to estimate risk (e.g., mortality in ICU) Predictive Analytics Enhancing Critical Care Outcomes ……Cont . Diwan et al. (2025)

17. Bedside Monitors Clinical Notes (NLP) Imaging and Public Databases (e.g., MIMIC-IV) Electronic Health Records (EHRs) ICU Data Mart M. Mesinovic et al. (2025) H. Wang (2025) Source of First-Day ICU Data

18. ICU Data Mart is a structured , comprehensive , and real-time clinical database specifically designed to support quality improvement, research, and artificial intelligence (AI) model development in the intensive care setting. Real-time data for predictive modeling Supports time- adjusted analysis and decision support Example: Mayo Clinic ICU Data Mart Johnson AEW, et al. (2021). ICU Data Mart

19. Machine Learning Deep Learning Time-series and multi-modal data integration Wang et al., 2025, Zhang et al., 2023 Al Models for Mortality Prediction

20. Define clinical problem Collect and preprocess data Feature selection Train and validate model Evaluate with AUROC , calibration Deploy with explainability tools EU AI Act (entered into force Aug 1, 2024) Al Models Development Steps

21. Personalized AI for COVID-19 ICU Admission & Mortality Performance: Mortality prediction: AUC 0.92 (sensitivity 0.89, specificity 0.82) ICU admission: AUC 0.89 (sensitivity 0.75, specificity 0.88 Jiayi Gao et al,BMG (2024 Sepsis Mortality Prediction : Performance: AUC 0.94 ± 0.01 Wang, S., Liu, X., Yuan, S. et al. . npj Digit.Med . 8, 228 (2025). An artificial intelligence model to predict mortality among hemodialysis patients: performance :AUC value of 0.892 (95% CI: 0.840–0.945). Peng, Z., Zhong, S.et al Published Al Models In ICU Prediction

22. Hypothetical comparison between traditional statistical analysis and AI-based models in the use of heparin-binding protein (HBP), for predicting early ICU mortality in ARDS patients Practical Application

23. Study Type: Prospective observational Subjects: 40 ARDS patients, 10 healthy controls Biomarker Measured: Heparin-binding protein (HBP) Measurement Days: Day 1 (HBP1) and Day 5 Practical Application The Original Study

24. HBP1 (Day 1): Diagnostic value (AUC=0.995, P=0.001) HBP1 not predictive of mortality (P=0.067) HBP5 (Day 5): Prognostic value (AUC=0.812, P=0.006) HBP5 significantly higher in non-survivors Practical Application The Original Study Results

25. Combined HBP1 with other variables for prediction Used medical ICU data mart to involve first day data in ICU other than HBP. May gain prognostic power via multi-feature analysis Captures hidden non-linear patterns. Al Model Using HBP1 to Predict Mortality

26. Uses first-day ICU data including HBP1, SOFA score, CRP, PaO ₂/ FiO ₂, etc. Employs machine learning algorithms . Even if HBP1 is not independently predictive, AI can detect non-linear associations and interactions. Capable of early prediction of ICU mortality without needing Day 5 measurements like HBP5. Offers real-time, patient-specific risk assessment. Al-Based Model (Hypothetical)

27. Feature Traditional Analysis AI-Based Model HBP1 Usage Diagnostic only Used for prediction in multi-variate models Mortality Prediction Needs Day 5 (HBP5) Possible using Day 1 data Data Integration Limited Multi-feature integration Pattern Detection Linear Non-linear, complex Outcome Personalization Generalized Patient-specific predictions Comparative Summary

28. While traditional analysis shows the diagnostic value of HBP1 and prognostic value of HBP5 , AI-based models can utilize first-day data, including HBP1, to enhance early prediction of ICU mortality. This highlights the potential of AI to improve decision-making and personalize critical care management. Conclusion

29. Artificial intelligence in the ICU transforms the first 24 hours from a period of Risk into an opportunity for Outcome-oriented intervention Predictive analytics combined with first-day ICU data has the potential to transform mortality prediction from static , retrospective assessment into dynamic, real-time decision support. For safe and effective deployment. AI models must follow internationally recognized guidelines Take Home Message

30. For your attention