An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums

By Jun Gao, Ninghao Liu, Mark A. Lawley, Xia Hu. 2017

29 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Chen
2. Yongfeng Zhang
3. D. Gunning
4. David William Aha
5. Xia Hu
6. Ninghao Liu
7. Giulia Vilone
8. Longo Luca

In Molecular Biology

1. Yongfeng Zhang
2. David William Aha
3. D. Gunning
4. Xu Chen
5. Giulia Vilone
6. Xia Hu
7. Longo Luca
8. Ninghao Liu

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “For example, Gao et al. [2017] studied the explainability of text classification in online healthcare forums, where a sentence is classified into three classes: medication, symptom, and background, and an interpretation method is also developed, where the decision rules can be explicitly extracted t”
• Contextual Outlier Interpretation (2017, methodology): “One straightforward way for outlier interpretation is to apply feature selection to identify a subset of features that distinguish outliers from normal instances [Knorr and Ng, 1999; Micenková et al., 2013; Duan et al., 2014; Vinh et al., 2016; Gao et al., 2017].”
• Explainable Artificial Intelligence: a Systematic Review (2020, background): “Discriminative Patterns [297] aims at interpreting a random forest model that classifies sentences according to their contents by extracting a ruleset that enables interpretation and gains insight of useful information in texts which corresponds to discriminative sequential patterns of words, or sen”
• DARPA's Explainable Artificial Intelligence (XAI) Program (2019, methodology): “2018) and text (Gao et al. 2017) data and is being applied to theXAI analytics challenge problem area.”