Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.
BMC Med Inform Decis Mak
; 24(1): 178, 2024 Jun 24.
Article
in En
| MEDLINE
| ID: mdl-38915008
ABSTRACT
OBJECTIVE:
This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS ANDMETHODS:
The index system was developed in foursteps:
(1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task.RESULTS:
The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets.DISCUSSION:
The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation.CONCLUSION:
The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electronic Health Records
/
Data Accuracy
/
Machine Learning
Limits:
Humans
Language:
En
Journal:
BMC Med Inform Decis Mak
Journal subject:
INFORMATICA MEDICA
Year:
2024
Document type:
Article
Country of publication: