Using cognition to resolve duplicacy issues in socially connected healthcare for smart cities

Abstract

Social web has transformed healthcare communication as patients reach out to seek support and advice by connecting with other patients, caregivers and healthcare professionals. The influx of health-related queries and the volume of answers within the medical forums is a testimony to this adaption. The scalability, natural interaction and dynamism of the continuously collected and connected user-generated social big data can support health assessment, intervention and provisioning to produce the best kind of cognitive smart city. On the flip side the use of social media for healthcare communication suffers from data deluge, lack of reliability and quality, confidentiality and privacy (location/personal) issues. Duplicate questions, that is, queries with similar semantics (meaning) corrupt the filtering mechanism, increase the response time and compromise with the quality of the answer too. This research puts forward solutions to resolve the key challenge of duplicacy within the medical community Question-Answering sites (Medical CQAs). We propose to solve the semantic question matching problem for duplicate question pair detection, using a hybrid deep learning model, which combines a Co-attention based Bi-Directional Long Short-Term Memory (Bi-LSTM) Siamese neural network and a Multi-layer perceptron classifier to output the probability of a similarity match between the two questions. Euclidean distance function is then used to compute the similarity between questions. The proposed model is validated on 100 question pairs which are scrapped from three featured groups, namely, ‘Irritable Bowel Syndrome’, ‘Anxiety Disorder’ and ‘Menopause’ of Patient.info community forum and an accuracy of 86.375% is observed. The results obtained are comparable to that of the Quora’s state-of-the-art results for duplicate detection.