Identifying smoking status of patients is vital for assessing their risk for a disease. With the rapid adoption of electronic health records (EHRs), patient information is scattered across various systems in the form of structured and unstructured data. In this study, we aimed to develop a hybrid system using rule-based, unsupervised and supervised machine learning techniques to automatically identify the smoking status of patients in unstructured EHRs. In addition to traditional features, we used per-document topic model distribution weights as features in our system. We also discuss the performance of our hybrid system using different feature sets. Our preliminary results demonstrated that combining per-document topic model distribution weights with traditional features improve the overall performance of the system.
For example, REF developed a model to automatically identify smoking status using a SVM model.
A preliminary study on automatic identification of patient smoking status in unstructured electronic health records
BioNLP@IJCNLP
Computer Science Medicine