Classification of Medline documents using Global Relevant Weighing Schema

Imambi, S. Sagar; Sudha, T.

doi:10.5120/1989-2679

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…PubMed comprises more that 22 million references that include the entire MEDLINE database and other types of citations, such as in-process citations, which provide records for articles before those records go through quality control and are indexed; citations to articles that are out-of-scope from certain MEDLINE journals; citations that precede the date that a journal was selected for MEDLINE indexing; and other works such as chapters and books that are likewise outside the purview of MEDLINE. 6 This repository of scientific literature provides a vast amount of text data that has helped researchers to implement their classification algorithms (Imambi & Sudha 2011).…”

Section: Text Gatheringmentioning

confidence: 99%

1. Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature

Neustein¹,

Imambi²,

Rodrigues³

et al. 2014

Text Mining of Web-Based Medical Content

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One of the tools that can aid researchers and clinicians in coping with the surfeit of biomedical information is text mining. In this chapter, we explore how text mining is used to perform biomedical knowledge extraction. By describing its main phases, we show how text mining can be used to obtain relevant information from vast online databases of health science literature and patients' electronic health records. In so doing, we describe the workings of the four phases of biomedical knowledge extraction using text mining (text gathering, text preprocessing, text analysis, and presentation) entailed in retrieval of the sought information with a high accuracy rate. The chapter also includes an in depth analysis of the differences between clinical text found in electronic health records and biomedical text found in online journals, books, and conference papers, as well as a presentation of various text mining tools that have been developed in both university and commercial settings.

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Section: Text Gatheringmentioning

confidence: 99%

1. Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature

Neustein¹,

Imambi²,

Rodrigues³

et al. 2014

Text Mining of Web-Based Medical Content

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“…The authors in (Imambi & Sudha, 2011) describe a new model for text classification using estimating term weights which improves accuracy classification according to the authors experiences. Documents are represented as vectors of terms with their normalized global frequency.…”

Section: Related Workmentioning

confidence: 99%

BioTextRetriever

Gonçalves

Camacho

Oliveira

2011

International Journal of Knowledge Discovery in Bioinformatics

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Whenever new sequences of DNA or proteins have been decoded it is almost compulsory to look at similar sequences and papers describing those sequences in order to both collect relevant information concerning the function and activity of the new sequences and/or know what is known already about similar sequences. In current web sites and data bases of sequences there are, usually, a set of curated paper references linked to each sequence. Those links are a good starting point to look for relevant information related to a set of sequences. One way to implement such approach is to do a blast with the new decoded sequences, and collect similar sequences. Then one looks at the papers linked with the similar sequences. Most often the number of retrieved papers is small and one has to search large data bases for relevant papers. This paper proposes a process of generating a classifier based on the initially set of relevant papers. First, the authors collect similar sequences using an alignment algorithm like Blast. Then, the authors use the enlarges set of papers to construct a classifier. Finally a classifier is used to automatically enlarge the set of relevant papers by searching the MEDLINE using the automatically constructed classifier.

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