Abstract—Individuals are increasingly turning to the web to seek and share healthcare information and this trend in online health information has resulted in a proliferation of user generated health centric content, especially online physician reviews. Physician rating websites can play a major role in empowering patients to make informed choices while selecting healthcare providers for advice and treatment. Given the wealth of information hidden in unstructured narratives such as online ratings, comments and clinical documents, there is a critical need for building efficient and accurate text classifiers for biomedicine corpus. In this paper, we analyze patient (dis)satisfaction using performance reviews of doctors and predict their ratings on various measures such as ‘Knowledgeability’, ‘Staff’ and ‘Helpfulness’. We explore solutions for the same problem using Convolutional Neural Networks trained on various optimization and loss functions. We analyze the 35000 user reviews available at “www.ratemds.com” for more than 10000 doctors. The proposed model obtained an accuracy of 93% for positive/negative binary classification of patient reviews. Moreover, we obtained a mean absolute error of 0.525 in predicting rating on a 5-point scale, thu, significantly improving upon the state of the art’s error rate of 0.71.
Index Terms—Text classification, sentiment analysis, convolutional neural networks, dropout, physician review.
Ranti Dev Sharma, Samarth Tripathi, Sunil Kumar Sahu, and Sudhanshu Mittal were with the Computer Science Department, Indian Institute of Technology Guwahati, India (e-mail: ranti.iitg@gmail.com, samarthtripathi@gmail.com, sunilitggu@gmail.com, sudhanshumittal1992@gmail.com).
Ashish Anand is with the Computer Science Department, Indian Institute of Technology Guwahati, India (e-mail: anand.ashish@iitg.ernet.in).
Cite: Ranti D. Sharma, Samarth Tripathi, Sunil K. Sahu, Sudhanshu Mittal, and Ashish Anand, "Predicting Online Doctor Ratings from User Reviews Using Convolutional Neural Networks," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 149-154, 2016.