A preliminary study on automatic identification of patient smoking status in unstructured electronic health records
Jitendra Jonnagaddala, Hong-Jie Dai, PRADEEP KUMAR RAY, Siaw‐Teng Liaw · 2015
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.