ArticleFrontiers in artificial intelligence2026
Unsupervised and explainable machine learning solutions for last-minute cancelations in pediatric ambulatory and day surgeries in Italy.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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16 authors.
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Abstract
Background: Canceled procedures in pediatric day-case surgery are a serious issue that can disrupt hospital workflow, resource utilization, and patient care. To better analyze this and explore strategies to mitigate such costly inefficiencies, we employed unsupervised machine learning (ML) techniques to analyze data from children undergoing day-case surgery at the Ospedale Pediatrico Bambino Gesù IRCCS between January 2020 and March 2022. Methods: We analyzed a dataset including 4,417 operated and 183 non-operated patients. The following variables were considered: age, hospitalization type, surgical specialty, procedure type, and reasons for cancelation. Dimensionality reduction was performed using Factor Analysis of Mixed Data (FAMD). Cluster analysis was conducted using the k-means algorithm, while a Random Forest classifier was employed to assess feature importance and enhance model interpretability. Results: The overall cancelation rate was 3.84%. Among operated patients, the median duration of surgery was 22 min (IQR: 12-32). For non-operated patients, the median waiting time from first consultation to the scheduled surgical procedure was 105 days (IQR: 58-194). K-means clustering ( Conclusion: The ML analysis may suggest targeted strategies for optimizing scheduling and resource allocation, ultimately improving patient care and operational efficiency.
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