No abstract

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.

Medline and Pubmed repositories are rich in medical literature .Once the documents are retrieved from PUBMED, they need further analysis. This paper describes new model for text classification by estimating terms weights and shows how the classification accuracy is improved with this method. The method uses global relevant weight as term weighing schema. Experiments performed with different weighing schemas shows that the new global relevant weighing method outperforms the traditional term weighing approaches.

Medical imaging technology has revolutionized health care over the past three decades allowing doctors to detect, cure and improve patient outcomes. Medicinal imaging makes picture of the internal organs, parts, tissues and bones for therapeutic examination and research pur-poses. It can likewise be utilized to think about elements of a few organs. X-ray and CT scanner are the two greatest after-effect of headway of imaging methods supplanting 2D procedures. X-ray is the standout amongst the most critical pre-processing ventures in tumor discovery. Magnetic resonance imaging (MRI) is really an imaging procedure in the restorative field. It is utilized as a part of radiology for imagining interior structures of the body and furthermore how they work. X-ray gives you the 3D picture of the inside bits of the body which enables the specialist to dissect the infection or tumor effortlessly though old imaging procedures like x-beam imaging gives you 2D pictures. In this paper we are introducing distinctive systems for distinguishing, preparing restorative pictures.

The exponential growth of online repositories in medical science has led to the development of various text mining tool . Theses tools assist the users in analyzing text data stored in the online repositories like Pubmed and medline. The pubmed repositories are growing at the rate of 500000 articles per year. Classification of Medline documents becomes very complex due to high dimensionality of feature space. In this study we discussed how dimensionality is reduced. We study and compared various dimensionality reduction techniques at the preprocessing stage. We introduce a novel feature weighting scheme "GRW " and proved that this schema improves classification accuracy. Our experimental results indicate that existing feature weighting methods has less accuracy rate when compared to GRW schema and tested on medical data set

The exponential growth of online repositories in medical science has led to the development of various text mining tool. Theses tools assist the users in analyzing text data stored in the online repositories like Pubmed and Medline. The pubmed repositories are growing at the rate of 500000 articles per year. Classification of Medline documents becomes very complex due to high dimensionality of feature space. In this study we discussed how dimensionality is reduced. We study and compared various dimensionality reduction techniques at the pre-processing stage. We introduce a novel feature weighting scheme 'GRW ' and proved that this schema improves classification accuracy. Our experimental results indicate that existing feature weighting methods has less accuracy rate when compared to GRW schema and tested on medical data set

No abstract

No abstract