Back to all news
AI Model Enhances Prediction of Infection Risks from Oral Mucositis in Stem Cell Transplant Patients
Researchers developed an explainable AI tool that accurately predicts infection risks related to oral mucositis in hematopoietic stem cell transplant patients.
Key Details
- 1Patients with oral mucositis after HSCT are almost 4x more likely to develop serious infections.
- 2A new AI-driven nomogram using demographic and clinical features shows superior predictive accuracy versus traditional models.
- 3Explainable AI provided clinicians with rationale for predictions, enabling targeted preventive care.
- 4Meta-analysis identified high-risk groups and specific risk factors such as chemotherapy types, age, and kidney issues.
- 5Researchers are working towards broad clinical adoption, including validation for other adverse events in cancer therapy.
- 6Findings were recently published in the journal Cancers and presented at MASCC 2025.
Why It Matters
This work leverages advanced AI to proactively identify high-risk cancer patients, supporting multidisciplinary care and personalizing preventive strategies. Enhanced prediction of adverse events can improve outcomes, reduce complications, and optimize resource use in cancer therapy.
Source
EurekAlert
Related News
•EurekAlert
AI Accelerates Radiopharmaceuticals, Boosts Personalized Dosimetry in Cancer
Machine learning is driving advancements in radiopharmaceutical drug discovery and optimizing patient-specific dosimetry for precision cancer therapy.
•EurekAlert
Physicians Overly Trust Erroneous AI, Ignore Contradictory Evidence
Physicians tend to trust incorrect AI advice, even when evidence contradicts it, suggesting risks in clinical decision-making with AI tools.
•EurekAlert
Concerns Raised Over Unverified Datasets in AI Health Prediction Models
A new study finds widely used AI health prediction models are built on datasets with unverifiable origins, raising safety and validity concerns.