Automatic Identification of Substance Abuse from Social History in Clinical Text

Substance abuse poses many negative health risks. Tobacco use increases the rates of many diseases such as coronary heart disease and lung cancer. Clinical notes contain rich information detailing the history of substance abuse from caregivers perspective. In this work, we present our work on automatic identification of substance abuse from clinical text. We created a publicly available dataset that has been annotated for three types of substance abuse including tobacco, alcohol, and drug, with 7 entity types per event, including status, type, method, amount, frequency, exposure-history and quit-history. Using a combination of machine learning and natural language processing approaches, our results on an unseen test set range from 0.51–0.58 F1 on stringent, full event, identification, and from 0.80–0.91 F1 for identification of the substance abuse event and status. These results indicate the feasibility of extracting detailed substance abuse information from clinical records.