Spatial Distribution Based Provisional Disease Diagnosis in Remote Healthcare

Patients in rural India cannot able to enquire about their health using appropriate disease related keywords, submitted as query. Lack of domain knowledge prevents the patients to refine the query using well-known feedback mechanism. Moreover, due to scarcity of doctors in rural India, the health assistants who run the health centers do not have enough knowledge to treat the patients based on the imprecise query. In the paper, we propose an autonomous provisional disease diagnosis system by classifying the query, which has been expanded using semantic of the domain knowledge. First, we apply spatial distribution based nearest neighbor spacing distribution (NNSD) on the disease related medical document corpus (MDC) to find the relevant terms, mostly symptoms with respect to different diseases. We frame a symptom vocabulary (SV) with the unique terms present in different diseases, known apriori. Each query is expanded as bag of symptoms (BoS) using 5-gram collocation model and log likelihood ratio (LLR) to measure the association between the query and the terms in the MDC. The terms in the BoS may not exactly match with the symptoms in the SV but have contextual similarity. We propose a novel approach to know which symptoms in the SV are nearest in context to the corresponding terms in the BoS. The feature vector is obtained by encoding the SV with respect to (w.r.t.) each BoS, which is sparse in nature. We apply sparse representation based classifier (SRC) to classify the query into a particular disease. Proposed nearest neighbor spacing distribution based sparse representation classifier (NNSD-SRC) shows promising performance considering MDC dataset and we validate the results with the doctors showing negligible error.