Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies. Materials and
The work given in REF deals with the automatic prediction of RA disease activity from the Electronic Medical Records (EMR data).
Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records
PLoS ONE
PLoS ONE
Medicine Computer Science