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Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification
| Content Provider | Scilit |
|---|---|
| Author | Kumaravelan, G. Behera, Bichitra Nanda |
| Copyright Year | 2020 |
| Description | Book Name: Handbook of Artificial Intelligence in Biomedical Engineering |
| Abstract | Document classification is a prevalent task in natural language processing with broad applications in the biomedical domain, including biomedical literature indexing, automatic diagnosis codes assignment, tweets classification for public health topics, patient safety reports classification, etc. In recent years, the categorization of biomedical literature plays a vital role in biomedical engineering. Nevertheless, manual classification of biomedical papers published in every year into predefined categories becomes a cumbersome task. Hence, building an effective automatic document classification for biomedical databases emerges as a significant task among the scientific community. Hence, this chapter investigates the deployment of the state-of-the-art machine learning (ML) algorithms like decision tree, k-nearest neighborhood, Rocchio, ridge, passive–aggressive, multinomial naïve Bayes (NB), Bernoulli NB, support vector machine, and artificial neural network classifiers such as perceptron, random gradient descent, BPN in automatic classification of biomedical text documents on benchmark datasets like BioCreative Corpus III (BC3), Farm Ads, and TREC 2006 genetics Track. Finally, the performance of all the said constitutional classifiers are compared and evaluated by means of the well-defined metrics like accuracy, error rate, precision, recall, and f-measure.This chapter focuses on the deployment of state-of-the-art supervised ML algorithms for biomedical text classification. It provides an overview of the deployment of the state-of-the-art supervised ML in biomedical text document classification. The major aim of the text classification process is to predict a class label of the given test document with the prior knowledge of trained dataset. In general, text classification process involves three important steps: text preprocessing, text classification, and postprocessing. Document classification is a prevalent task in natural language processing with broad applications in the biomedical domain, including biomedical literature indexing, automatic diagnosis codes assignment, tweets classification for public health topics, patient safety reports classification, etc. Biomedical engineering introduces different innovative techniques and materials in medicine and healthcare for the development of novel biomedical tools. Biomedical text classification deals with unstructured text documents from different biomedical repositories like PubMed and MedLine, web blogs, e-newspapers, medical reports, and social media. |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2020-0-12878-4&isbn=9781003045564&format=googlePreviewPdf |
| Ending Page | 422 |
| Page Count | 22 |
| Starting Page | 401 |
| DOI | 10.1201/9781003045564-18 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-28 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Handbook of Artificial Intelligence in Biomedical Engineering Biomedical Engineering Medical Informatics Safety Biomedical Artificial Neural Network Natural Language Processing Classification for Public Processing with Broad Automatic Diagnosis Codes |
| Content Type | Text |
| Resource Type | Chapter |
