{"id":3189,"date":"2019-12-03T09:19:48","date_gmt":"2019-12-03T09:19:48","guid":{"rendered":"http:\/\/tutorsindia.com\/academy\/?p=3189"},"modified":"2022-12-15T10:30:05","modified_gmt":"2022-12-15T10:30:05","slug":"recent-trends-of-nlp-based-text-analytics-with-specific-to-the-semantic-information-medical-application","status":"publish","type":"post","link":"https:\/\/www.tutorsindia.com\/academy\/engineering-and-techology\/recent-trends-of-nlp-based-text-analytics-with-specific-to-the-semantic-information-medical-application\/","title":{"rendered":"Recent Trends Of NLP Based Text Analytics (With Specific To The Semantic Information- Medical Application)"},"content":{"rendered":"\n

The advancements in medical sector have been exhibited in different technological aspects (Yang et al., 2015). However, with the advancements there have also been noticed certain challenges such as higher cost, increase in complexity, poor quality of care etc (Cortada, Gordon, & Lenihan, 2012). For example, considering U.S. healthcare sector it has been noticed that 21-47% of the expenditures Berwick & Hackbarth, (2012) occur due to inefficiency such inappropriate use of antibiotics etc. 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 (Makary & Daniel, 2016). Thus, to alleviate these issues employing better decision-making techniques on the basis of information available can be considered to be an optimal solution. Consequently, the health care<\/a> sector is employing information technology in their management systems (Prokosch & Ganslandt, 2009).<\/p>\n\n\n\n

While implementation of information technology has enabled gathering a vast amount of data, employing analytics tools and techniques has enabled retrieving the relevant or desired information from this unstructured data and then transforming it for enabling better decision-making process (Islam, Hasan, Wang, Germack, & Noor-E-Alam, 2018). Employing analytics tools and techniques such as data mining, text mining etc has been of great help for medical professionals in predicting and diagnosing diseases, treating patients, consequently leading to delivering better service quality and reducing the expenses (Tomar & Agarwal, 2013). As per the estimates, applying data mining can enable saving $450 billion every year from the U.S. medical sector (Herland, Khoshgoftaar, & Wald, 2014).<\/p>\n\n\n\n

Noticeably, exploitation of e-health records and the corresponding interest in employing them for optimization of data quality has exhibited that analyzing the open text (data captured in health records) is highly critical (Dhole & Uke, 2014). There have been used several techniques for extracting medical text. For example, natural language processing technique employs different tools such as part of speech taggers, noun entity recognizers etc. While applying these tools, it has been noticed that medical text differs from normal text as it includes complex terminologies.<\/p>\n\n\n\n

These type of texts usually include abstracts of scientific papers, medical reports etc. 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 (Moreno & Redondo, 2016). Recognition of biomedical entities includes identifying and grouping the entity names as per the different medical domains such as proteins, genes, drugs etc. Though 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 .<\/p>\n\n\n\n

Linguistic-based\napproaches: This approach is based on using parsers to understand syntactic\nstructures and associate them into semantic representations.<\/p>\n\n\n\n

\u2022 Pattern-based approaches: This approach employs a set of patterns for associating the entities that are defined by domain experts. <\/p>\n\n\n\n

\u2022 Machine Learning-based approaches: This approach is based on deriving associations in new categories that represent similar text (Andreu-Perez, Poon, Merrifield, Wong, & Yang, 2015) <\/p>\n\n\n\n

To summarize, it can be asserted that NLP enables a machine to process the human language and transform it to machine-understandable format. It is noteworthy that the origination of NLP took place in 1960s but it soon gained popularity with the large scale employability of the World Wide Web and search engines. <\/p>\n\n\n\n

The\nprocess of processing followed by NLP is divided into the following steps:<\/p>\n\n\n\n