The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions

Preview this chapter:

The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions, Page 1 of 1

< Previous page | Next page > /docserver/preview/fulltext/9789815305395/chapter-14-1.gif

An integral aspect of natural language processing is text categorization, the goal of which is to assign a predetermined category to a given text. Feature selection and categorization models come in a wide variety of forms. Most researchers, however, would rather utilise the prepackaged functions of existing libraries. In the field of natural language processing (NLP), automated medical text categorization is very helpful for decoding the information hidden in clinical descriptions. Machine learning approaches seem to be fairly successful for medical text categorization problems; nevertheless, substantial human work is required in order to provide labelled training data. Clinical and translational research has benefited greatly from the computerised collection of vast amounts of precise patient information, including illness status, blood tests, medications taken, and side effects, along with therapy results. As a result, the medical literature contains a massive amount of information on individual patients, making it very difficult to digest. In this research, we suggest using N-grams and a Support Vector Machine (SVM) to classify healthcare-related texts. We conduct experiments to determine the viability of our code and analyse it across a variety of categorization methods.