Clinical Text Classification of Medical Transcriptions Based on Different Diseases

Title

Clinical Text Classification of Medical Transcriptions Based on Different Diseases

Creator

Sreekumar Y.; Nizar Banu P.K.

Description

Clinical text classification is the process of extracting the information from clinical narratives. Clinical narratives are the voice files, notes taken during a lecture, or other spoken material given by physicians. Because of the rapid rise in data in the healthcare sector, text mining and information extraction (IE) have acquired a few applications in the previous few years. This research attempts to use machine learning algorithms to diagnose diseases from the given medical transcriptions. Proposed clinical text classification models could decrease human efforts of labeled training data creation and feature engineering and for designing for applying machine learning models to clinical text classification by leveraging weak supervision. The main aim of this paper is to compare the multiclass logistic regression model and support vector classifier model which is implemented for performing clinical text classification on medical transcriptions. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Electrical Engineering, Vol-853, pp. 613-623.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Clinical text mining; Multiclass logistic regression; Natural language processing; scispaCy; Support vector classifier; TF-IDF vectorization; Transcriptions

Coverage

Sreekumar Y., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 18761100; ISBN: 978-981169884-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-16-9885-9_50

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127778525&doi=10.1007%2f978-981-16-9885-9_50&partnerID=40&md5=e3ec137de2ed46c17d9fbe551896b445