A Clinician's Guide to Running Custom Machine-Learning Models in an Electronic Health Record Environment.
Mayo Clin Proc
; 98(3): 445-450, 2023 03.
Article
de En
| MEDLINE
| ID: mdl-36868752
ABSTRACT
We recently brought an internally developed machine-learning model for predicting which patients in the emergency department would require hospital admission into the live electronic health record environment. Doing so involved navigating several engineering challenges that required the expertise of multiple parties across our institution. Our team of physician data scientists developed, validated, and implemented the model. We recognize a broad interest and need to adopt machine-learning models into clinical practice and seek to share our experience to enable other clinician-led initiatives. This Brief Report covers the entire model deployment process, starting once a team has trained and validated a model they wish to deploy in live clinical operations.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Course à pied
/
Dossiers médicaux électroniques
Type d'étude:
Prognostic_studies
Limites:
Humans
Langue:
En
Journal:
Mayo Clin Proc
Année:
2023
Type de document:
Article