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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Medical texts into medical terminologies/ontology’s by combining several natural language processing methods, such as stemming, morphological analysis, lexicon augmentation, term composition and negation detection. However, these methods are purely applicable to well-constructed discourses.

Machine learning approaches build inference models from medical data with known annotations and then apply the trained models to unseen data for terminology prediction.

PROPOSED CONCEPT:-

This paper presents a novel scheme to code the medical records by jointly utilizing local mining and global learning approaches, which are tightly linked and mutually reinforced.

Local mining attempts to code the individual medical record by independently extracting the medical concepts from the medical record itself and then mapping them to authenticated terminologies

EXISTING TECHNIQUE:-

Rule-based and machine learning approaches

PROPOSED TECHNIQUE:-

Pre-clustering technique

TECHNIQUE DEFNITION:-

Rule-based approaches play a principle role in medical terminology assignments.

Machine learning approaches build inference models from medical data with known annotations and then apply the trained models to unseen data for terminology prediction.

TECHNIQUE DEFNITION:-

Pre-clustering technique approach suffers from three limitations: information loss, lower precision and over-widened terminology space problems. This subsection aims to discuss how global learning approach breaks these barriers.

DRAWBACKS:-

User didn’t get Proper answer from experts in the Same Question.

The answer part is not displayed due to the space limitation

ADVANTAGES:-

Medical records have been accumulated in their repositories, and in most circumstances, users may directly locate good answers by searching from these record archives, rather than waiting for the experts’ responses or browsing through a list of potentially relevant documents from the Web.