Achieving high inter-rater reliability in establishing data labels: a retrospective chart review study
Background In medical research, the effectiveness of machine learning algorithms depends heavily on the accuracy of labeled data. This study aimed to assess inter-rater reliability (IRR) in a retrospective electronic medical chart review to create high quality labeled data on comorbidities and adver...
Main Authors: | Hude Quan, Danielle A Southern, Yuan Xu, Cathy Eastwood, Guosong Wu, Natalie Sapiro, Cheligeer Cheligeer |
---|---|
Format: | Article |
Language: | English |
Published: |
BMJ Publishing Group
2024-04-01
|
Series: | BMJ Open Quality |
Online Access: | https://bmjopenquality.bmj.com/content/13/2/e002722.full |
Similar Items
-
Improving Detection of Hospital Adverse Events Using Machine Learning on Real-World Narrative EMR Data
by: Cheligeer Cheligeer, et al.
Published: (2024-09-01) -
Administrative health data validity: Changes over 19 years
by: Jie Pan, et al.
Published: (2024-09-01) -
Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study
by: Guosong Wu, et al.
Published: (2023-09-01) -
Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction
by: Jie Pan, et al.
Published: (2024-09-01) -
Health Data Governance for Research Use in Alberta
by: Namneet Sandhu, et al.
Published: (2023-10-01)