Assessment of the risk of bacteremia in patients with hematologic malignancies in the emergency department: A comparative study between logistic regression and machine learning algorithms - PubMed
6 hours ago
- #Machine Learning
- #Emergency Medicine
- #Bacteremia
- The study compared logistic regression and machine learning algorithms for predicting bacteremia risk in hematologic malignancy patients in the emergency department.
- Three models were tested: BOSS-1 (multivariable logistic regression), BOSS-2 (K-means clustering), and BOSS-3 (support vector machine).
- BOSS-1 had high sensitivity (94%) for low-risk patients but low specificity (30%), while BOSS-2 improved high-risk identification with similar specificity.
- BOSS-3 classified patients into only low-risk (66.8%) or high-risk (33.2%) groups, showing the best balance of sensitivity (61%) and specificity (71%).
- External validation found BOSS-3 had the most reproducible results, indicating supervised machine learning has the greatest clinical potential.