Gao, Jun (2017-12). An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums. Master's Thesis.
Online Healthcare Forums (OHFs) have become increasingly popular for patients to share their health-related experiences. The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients. To extract the meaningful information of a post, a common way is to classify the sentences into several pre-defined categories of different semantics. However, the unstructured form of online posts brings challenges to existing classification algorithms. In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications. To tackle the challenges above, we propose an effective and interpretable OHF post classification framework. Specifically, we classify sentences into three classes: Medication, Symptom, and Background. Each sentence is projected into an interpretable feature space. A forest-based model is developed for categorizing OHF posts. An interpretation method is also developed, where the decision rules can be explicitly extracted to gain an insight of useful information in texts. Experiments and an application system will be implemented based on the proposed framework.