Timeline Registration for Electronic Health Records.
AMIA Jt Summits Transl Sci Proc
; 2023: 291-299, 2023.
Article
de En
| MEDLINE
| ID: mdl-37350882
ABSTRACT
Electronic Health Record (EHR) data are captured over time as patients receive care. Accordingly, variations among patients, such as when a patient presents for care during the course of a disease, introduce bias into standard longitudinal EHR data analysis methods. We, therefore, aim to provide an alignment method that reduces this bias. We structure this task as a registration problem. While limited prior research on longitudinal EHR data considered registration, we propose a robust registration method to provide better data alignment by estimating the optimum time shift at each time point. We validate the proposed method for mortality prediction. We utilize a Recurrent Neural Network (RNN), time-varying Cox regression model, and Logistic Regression (LR) for mortality prediction. Results suggest our proposed registration method enhances mortality prediction with at least a 1-2% increase in major evaluation metrics utilized.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Type d'étude:
Prognostic_studies
Langue:
En
Journal:
AMIA Jt Summits Transl Sci Proc
Année:
2023
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique