Published Paper Details:

In the era of Web 2.0, questions and answers play an important role to provide an alternative way for knowledge acquisition through the internet. Question retrieval in context to the present community-based question answering (CQA) services does not work well in case of long and complex queries, like the questions. Some of the reasons for it are the verboseness in natural language queries and also the word mismatch that occurs in the queries and the candidate questions in the CQA archive in the process of retrieval. Question retrieval is commonly used in hospital management system as a prediction tool so as to classify the risks/diseases. It aims at delivering a quick and direct solution to patients that automatically produces the respective health advisory which includes name of the disease and the risks associated with the same. In order to analyze the risk factor an additional medical related database must be used (i.e., a dataset that holds 3000 Entries on the various symptoms and its related data). Once the patient enters the required details about the sickness, the duration of the Symptom existing, the Frequency of the sickness and the affected place etc. The system will then immediately analyze the details and begins to classify the problem and its risks. When the risk exceeds the quotedthreshold, i.e., when the case is observed to be critical and needs immediate medical attention, then a list of available doctors concerned to the illness is broadcasted to the patient in order to fix appointments. In some cases, the Doctor might recommend some tests and diagnosis to be done in the laboratory. The laboratory feature/module permits the patient to see the recommended tests that are suggested by the doctors. The lab results will be communicated to both the patient and the doctor by lab personnel using this functionality.