Public articles linked to the same research event.
Health Care Management Science Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.
Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.
Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.
Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.