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 healthrelated 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 usergenerated 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 (locationpersonal) 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 QuestionAnswering 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 Coattention based BiDirectional Long ShortTerm Memory (BiLSTM) Siamese neural network and a Multilayer 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, xe2x80x98Irritable Bowel Syndromexe2x80x99, xe2x80x98Anxiety Disorderxe2x80x99 and xe2x80x98Menopausexe2x80x99 of Patient.info community forum and an accuracy of 86.375% is observed. The results obtained are comparable to that of the Quoraxe2x80x99s stateoftheart results for duplicate detection.

Knowledge Abstraction Matching for Medical Question Answering Medical Question Answering (medical QA), which studies the problem of automatically answering patientsu0027 medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment  N gram mining, medical knowledge abstraction, medical segment matching and answer reretrieval. KAM has been incorporated into Baiduu0027s enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.