| خلاصه مقاله | Organ donation from brain-dead patients is crucial for saving lives, but selecting
suitable donors is a complex process. Poisoning, a significant cause of brain death,
introduces unique challenges. Traditional donor selection relies heavily on
clinical judgment and historical data, which can be subjective and may
overlook subtle factors affecting organ viability.This hypothetical study
(as detailed, comprehensive real-world data on this specific topic is not
publicly available) would utilize a retrospective cohort design. Data would be
collected from multiple sources: Medical Records , Organ Procurement
Organization Data , Laboratory Data.(Hypothetical results based on potential findings)
Feature importance analysis revealed that specific biochemical markers , duration
of exposure to the toxin, and the presence of specific co-morbidities were strong
predictors of organ viability. The model’s predictions led to a statistically
significant increase in the number of successfully transplanted organs, potentially
reducing the waiting list for organ recipents.
Furthermore, providing valuable insights into
improving organ preservation techniques and potentially expanding the donor pool.
AI offers a powerful tool for optimizing organ donation selection in brain-death
patients resulting from poisoning. Future studies should focus on validating these
findings in larger, more diverse datasets, developing user-friendly clinical decision
support tools incorporating this technology, and further investigating the
ethical implications of AI-driven organ
allocation.Artificial Intelligence |