Empowered Analysis Using Artificial Intelligence Algorithms for a New Era of Health Sector Preparedness and Response Strategies to Chemical, Biological, Radiological, and Nuclear Major Incidents in the Middle East and North Africa
Bibliographic record
Abstract
Background/Introduction: Chemical, biological, radiological, and nuclear (CBRN) incidents pose increasing transborder risks globally, necessitating enhanced health sector preparedness. Objectives: This study aimed to develop a comprehensive CBRN preparedness assessment tool (PAT), operational response guidelines (ORG), and tabletop simulation scenarios for the health sectors of the Middle East and North Africa (MENA) region. Method/Description: A mixed-methods approach comprised a systematic review of the literature up to 2022 in English and French, modified expert interviews (MIM), and an online Delphi questionnaire. Content analysis was performed on interview data. Using R-Studio™, consensus metrics and artificial intelligence techniques, including natural language processing, sentiment analysis, and unsupervised machine learning (ML) clustering algorithms, were deployed for advanced data analysis across all phases. Results/Outcomes: The literature review identified 63 relevant studies illustrating various preparedness strategies. The MIM’s thematic analysis, reinforced by AI-driven content analysis, emphasized the need for stronger inter-regional cooperation facilitated by organizations such as WHO and standardized tabletop simulation training. A robust consensus was achieved on the proposed assessment tool and operational response guidelines. ML analysis identified distinct expert clusters, providing additional consensus perspectives. Conclusion: The study emphasized the urgency for collaborative CBRN response strategies within MENA, valuing the innovative aspect of our suggested PAT, ORG, and simulation scenarios. This work advocates a dynamic, resilient approach to disaster medicine preparedness, which is crucial for regional security and global health resilience, especially in the MENA. It also highlights the significant role of AI analysis methods in enriching analytical outcomes in disaster medicine research and promoting data-informed preparedness strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".