Validation of a machine learning approach to estimate Systemic Lupus Erythematosus Disease Activity Index score categories and application in a real-world dataset.
RMD Open
; 7(2)2021 05.
Article
em En
| MEDLINE
| ID: mdl-34016712
ABSTRACT
OBJECTIVE:
Use of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) in routine clinical practice is inconsistent, and availability of clinician-recorded SLEDAI scores in real-world datasets is limited. This study aimed to validate a machine learning model to estimate SLEDAI score categories using clinical notes and to apply the model to a large, real-world dataset to generate estimated score categories for use in future research studies.METHODS:
A machine learning model was developed to estimate an individual patient's SLEDAI score category (no activity, mild activity, moderate activity or high/very high activity) for a specific encounter date using clinical notes. A training cohort of 3504 encounters and a separate validation cohort of 1576 encounters were created from the OM1 SLE Registry. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calculated using a binarised version of the outcome that sets the positive class to be those records with clinician-recorded SLEDAI scores >5 and the negative class to be records with scores ≤5. Model performance was evaluated by categorising the scores into the four disease activity categories and by calculating the Spearman's R value and Pearson's R value.RESULTS:
The AUC for the two categories was 0.93 for the development cohort and 0.91 for the validation cohort. The model had a Spearman's R value of 0.7 and a Pearson's R value of 0.7 when calculated using the four disease activity categories.CONCLUSION:
The model performs well when estimating SLEDAI score categories using unstructured clinical notes.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Lúpus Eritematoso Sistêmico
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article