Abstract：
: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 meaning of a post, a commonly used way is to classify the sentences into several predefined 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 consisting of labeled sequential patterns, UMLS semantic types, and other heuristic features. 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. Experimental results on real-world OHF data demonstrate the effectiveness of our proposed computational framework.
journal_name
J Healthc Engjournal_title
Journal of healthcare engineeringauthors
Gao J,Liu N,Lawley M,Hu Xdoi
10.1155/2017/2460174subject
Has Abstractpub_date
2017-01-01 00:00:00pages
2460174eissn
2040-2295issn
2040-2309journal_volume
2017pub_type
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