Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
J Biomed Inform
; 68: 112-120, 2017 04.
Article
en En
| MEDLINE
| ID: mdl-28323112
ABSTRACT
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_sistemas_informacao_saude
Asunto principal:
Infección de la Herida Quirúrgica
/
Recolección de Datos
/
Registros Electrónicos de Salud
/
Mejoramiento de la Calidad
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Biomed Inform
Asunto de la revista:
INFORMATICA MEDICA
Año:
2017
Tipo del documento:
Article
País de afiliación:
Estados Unidos