Extracting Modifiable Risk Factors from Narrative Preventive Healthcare Guidelines for EHR Integration

General criteria of preventive healthcare based on the preventive care guidelines have been integrated with Electronic Health Record (EHR) systems through decision support systems and led to improved performance in healthcare delivery. Advanced integration which considers factors such as ethnicity, social history, medical history, family history need to be investigated. Integrating the preventive healthcare guidelines with the EHR based on above factors requires the extraction of the relevant information from these guidelines using text mining and natural language processing techniques. In this research, we propose a framework to extract information according to the EHR modules. Our results show that the proposed framework successfully extracts terms and concepts, and adequately maps them to the proposed data interchange structure that is based on the EHR functional modules. The extracted information and the populated data interchange structures eases the integration of the modifiable risk factors with the patient's records in the EHR. The proposed framework can be extended to other clinical healthcare guidelines where modifiable risk factors are critical.