Prospective silent deployment and evaluation of an intelligent machine learning model for prediction of emergency department visits during cancer treatment.
Notice bibliographique
Résumé
407 Background: Patients undergoing cancer treatment often need to visit the emergency department (ED), straining the healthcare system. We aim to use long-term electronic health record (EHR) data to deploy and evaluate a previously built warning system designed to accurately identify those at risk of ED visits. This system, once validated, will enable clinicians to take early, personalized actions to prevent these ED visits, saving resources and enhancing the quality of life for cancer patients. Methods: Machine learning models were trained usinglongitudinal retrospective EHR data from patients receiving intravenous systemic therapies for gastrointestinal cancers at the Princess Margaret Cancer Centre, aiming to predict ED visits within 30 days of each treatment. Treatments followed by ED visits within one day were excluded to ensure the system focuses on detecting early warning signs rather than imminent ED visits. The models, including tree-based methods and neural networks, were tuned with Bayesian hyperparameter optimization and calibrated using isotonic regression. A temporal split was applied to establish a held-out retrospective test cohort. The best model was silently deployed for prospective validation in patients with gastrointestinal cancer through our internally developed 'MIRA' platform that supports clinical integration. Within MIRA, the patients' EHR data with treatments scheduled the next day are extracted from the EHR system and forwarded to the model for analysis. Results: In the retrospective cohort from January 1, 2014, to December 31, 2019, 1,997 patients underwent 24,350 treatments, with 2,219 (9.11%) leading to ED visits within 30 days. The top-performing model, an extreme gradient boosting tree, achieved an area under the receiver operating characteristic curve (AUROC) of 0.68 and an area under the precision-recall curve (AUPRC) of 0.19 in the held-out test set. Although the evaluation of the system through prospective silent deployment is ongoing, here we report on patients with treatments during March 2024, with adequate 30-day follow up by April 30th, 2024. During this period, 357 patients received 676 treatments, with ED visits within 30-days following 60 (8.88%) treatments. The deployed system achieved an AUROC of 0.66 (confidence interval: 0.60-0.72) and an AUPRC of 0.22 (confidence interval: 0.15-0.32), which closely aligns with those observed during its retrospective testing. At a 10% alarm rate, model has a positive predictive value of 0.33 and sensitivity of 0.22. Conclusions: During a silent prospective deployment, our system predicted ED visits in cancer patients undergoing medical treatment. These findings indicate that the system should be integrated into the clinical workflow and combined with interventions to prevent ED visits.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».