Abstract
The Telemedicine (also referred to as “telehealth” or “e-health”) Permits the healthcare specialists to assess, diagnose and deal with patients in remote places using telecommunications, particularly for those who live in rural or any underserved places. The primary care doctor takes initial symptoms of the patient after which electronically transmit them to the consultant by electronic mail or protected services from distant place particularly from rural or any underserved place on daily basis. It is impossible for a physician to manipulate on all the reports and then reply the emails with diagnosis and suggestions regularly. In this Research, we will generate automated tool which will measure the similarity between the different reports of patients which is in natural language. Our research is all about designing and implementing a theory that can read, understand and analyze the reports of patients in different data sets, written in the natural language in text form and grouped them into different categories on the basis of their similarity and dissimilarity. It will be helpful for the physicians to manipulate a number of reports and answer them with suggestions on daily basis.
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Iram, A., Gill, S.H. (2019). Similarity Measuring for Clustering Patient’s Reports in Telemedicine. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_4
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DOI: https://doi.org/10.1007/978-981-13-6052-7_4
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