Recent trends of NLP based text analytics with specific to semantic Information medical application

Research paper

RECENT TRENDS OF NLP BASED TEXT ANALYTICS (WITH SPECIFIC TO THE SEMANTIC INFORMATIONMEDICAL APPLICATION)

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The advancements in medical sector have been exhibited in different technological aspects.

BACK GRO UND

For example, considering U.S. healthcare sector it has been noticed that 21-47% of the expenditures occur due to inefficiency such inappropriate use of antibiotics etc.

With the advancements there have also been noticed certain challenges such as higher cost, increase in complexity, poor quality of care etc .

To alleviate these issues employing better decision-making techniques on the basis of information available can be considered to be an optimal solution.

It was also observed that a small part of these costs was related to low-quality care due to which many people die in the U.S. every year.

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Implementation Of I n f o r m a t i o n Te c h n o l o g y

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While implementation of information technology has enabled gathering a vast amount of data, employing analytics tools.

Techniques has enabled retrieving the relevant or desired information from this unstructured data and then transforming it for enabling better decision-making process.

Employing analytics tools and techniques has been of great help for medical professionals in predicting and diagnosing diseases.

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As per the estimates, applying data mining can enable saving $450 billion every year from the U.S. medical sector.

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RECOGNITION OF BIOMEDICAL 01

The primary task of the techniques used is classification of papers into different categories such that it can be fed into a database and thus retrieved by the end-user as required.

Recognition of biomedical entities includes identifying and grouping the entity names as per the different medical domains.

03

02

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There are numerous sources available, there is not a particular resource that can be considered as new medicines are explored every time and this imposes as the main hindrance for NLP.

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P R I M A RY M E T H O D O L O G I E S A S S O C I AT I N G M E D I C A L E N T I T I E S NLP offers three primary methodologies for associating the medical entities. They are as follows: •Linguistic-based approaches: It is based on using parsers to understand syntactic structures and associate them into semantic representations.

•Pattern-based approaches: It employs a set of patterns for associating the entities that are defined by domain experts.

•Machine Learning-based approaches: It is based on deriving associations in new categories that represent similar text.

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The Categorization process of processing followed by NLP is divided into the following steps: • Text Preprocessing/Tokenization • Lexical Analysis • Syntactical Analysis • Semantic Analysis

Categorization of NLP

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The healthcare sector is also concerned about extracting the relevant data from different sources or the data which is stored in different databases.

Extraction of relevant data from the unstructured data is another challenge revolving around the healthcare or medical domain.

Thus, considering this text mining technique is used to extract structured information from unstructured data.

H e a l t h c a re S e c t o r In the last few years, the amount of information generated regularly in the field of medicine is growing continuously, especially the information produced by professionals in their day to day routine.

Due to the presence of this unstructured data, the process of obtaining structured or relevant data for decision-making sometimes becomes more complex.

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CONCLUSIONS

Thus to conclude, it can be affirmed that the primary challenge encountered by different medical professionals is not just retrieving any data.

The different phases involved for extracting useful medical data from the unstructured data are as follows: Pre-processing stage: In this stage, unstructured data is arranged as per the standards and then processed further using NLP techniques.

Text representation stage: In this stage, unstructured data is converted into an appropriate form which enables it to be analyzed efficiently.

Discovery stage: This is the last stage of processing unstructured data, and in this stage, the information expected by the end-user is obtained by employing techniques such as clustering, classification etc.

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D ATA A N A LY T I C S - A P P L I C AT I O N S The continuous advancement of data analytics and its applications to medicine has served to be a great support for the medical professionals and patients as well. Some of these benefits are listed as follows:

Better quality of care services offered to patients.

Reduced medical errors.

Enabling prevention and detection of diseases in an earlier stage.

Facilitating the availability of most beneficial treatment for patients.

Reducing the consumption of both time as well as expenditure incurred by healthcare management.

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