Analyzing and Assisting Patient Decision-Making in Online Health Community
In recent years, many users have joined online health communities (OHC) to seek information, suggestions and social support. However, little has been studied about the roles of OHCs in patients' decision-making processes. The aim of this research is to analyze OHC data to better understand patient decision-making processes, and to provide assistance to OHC users in their decision-making.
In order to analyze or assist patients in their decision-making, a novel classification model is designed to identify discussion threads in OHC that are related to decision making. This is achieved by building a two-step combined deep learning model. Empirical evaluation shows the effectiveness of the approach. Using the new model, we found 46.9% of threads in the breast cancer discussion forum in Cancer Survivors Network are related to decision-making, demonstrating the significant role that OHC plays in patient decision making. To understand what patients consider during their decision making processes, topic modeling techniques are used to analyze the concern factors that patients expressed in those threads.
For users seeking help in OHC in their decision-making processes, the influences received are analyzed by developing a framework and deep-learning techniques to identify influence relationships among posts. The state-of-the-art text relevance measurement methods are leveraged to generate sparse feature vectors to present the text relevance. The probability of question and action presence in a post are modeled as dense features. Then deep learning techniques are leveraged to combine the sparse and dense features to learn the influence relationships. Empirical evaluation demonstrates the effectiveness of the approach.
Finally, to assist patient decision-making process, a personalized thread recommender system is developed to help users find relevant discussions without the burden of information overload. The system captures user interests in two dimensions: topics and concepts. Topic dimension is summarized through topic modeling techniques and concept dimension is encoded by a Convolutional Neural Network. A thread neural network is built to capture thread characteristics, and a user neural network is built to capture user interests as well as interest shifts over time using a Long Short-Term Memory Model. At last, user interests and thread characteristics are matched to make recommendations. Experimental evaluation with multiple OHC datasets demonstrates the performance advantage over the state-of-the-art recommender systems on various evaluation metrics.