Information Extraction from Clinical Documents: Towards Disease/Disorder Template Filling

In recent years there has been an increase in the generation of electronic health records EHRs, which lead to an increased scope for research on biomedical literature. Many research works have been using various NLP, information retrieval and machine learning techniques to extract information from these records. In this paper, we provide a methodology to extract information for understanding the status of the disease/disorder. The status of disease/disorder is based on different attributes like temporal information, severity and progression of the disease. Here, we consider ten attributes that allow us to understand the majority details regarding the status of the disease/disorder. They are Negation Indicator, Subject Class, Uncertainty Indicator, Course Class, Severity Class, Conditional Class, Generic Class, Body Location, DocTime Class, and Temporal Expression. In this paper, we present rule-based and machine learning approaches to identify each of these attributes and evaluate our system on attribute level and system level accuracies. This project was done as a part of the ShARe/CLEF eHealth Evaluation Lab 2014. We were able to achieve state-of-art accuracy 0.868 in identifying normalized values of the attributes.