ItemConference Paper

Machine Learning and Word Representation Techniques in Medical Transcription Data Analysis

Title
Machine Learning and Word Representation Techniques in Medical Transcription Data Analysis
Creator
Danties, Anna; Nizar Banu, P.K.; Azar, Ahmad Taher; Kamal, Nashwa Ahmad; Tounsi, Mohamed; Njima, Chakib Ben
Description
Dermatology is the branch of medicine that deals with the diagnosis, treatment, and prevention of skin diseases. Dermatological diseases can be difficult to diagnose, treat, and manage because there are several skin conditions, each with its unique set of symptoms and causes. Underlying medical conditions, environmental causes, or hereditary characteristics can cause complex skin problems. Furthermore, because skin problems can present in a variety of ways, obtaining an appropriate diagnosis and efficient treatment may be difficult. Treating dermatological disorders is a difficult endeavor. This article proposes an integrated model to assist people in understanding and discussing the nature of dermatology. This model's capabilities include text pre-processing, audio-to-text translation, named entity recognition (NER) for extracting crucial information, and text clustering and classification based on content. The necessity for precise and efficient analysis of large amounts of text data, notably the identification and standardisation of abbreviations and the extraction of relevant information, has been identified as a problem in dermatology and medical transcription. By grouping similar cases, clustering can make it easier to spot patterns and trends in dermatological disorders. However, classification can help automatically group text data into pre-established categories, such as various kinds of skin conditions or treatments. These methods simplify data analysis, increase accuracy, and assist healthcare professionals in reaching accurate conclusions regarding patient care. This article explores the partitioning algorithm for clustering, while logistic regression is used in classification. The model analysed in this article helps dermatologists and patients understand and manage skin problems. 2025 IEEE.
Source
2025 International Conference on Control, Automation and Diagnosis, ICCAD 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Logistic Regression; Named Entity Recognition (NER); Natural Language Processing (NLP); Partitioning Clustering
Coverage
Danties A., Christ University, Department of Computer Science, Bangalore, India; Nizar Banu P.K., Christ University, Department of Computer Science, Bangalore, India; Azar A.T., Prince Sultan University, College of Computer and Information Sciences, Riyadh, 11586, Saudi Arabia, Prince Sultan University, Automated Systems and Computing Lab (ASCL), Riyadh, Saudi Arabia; Kamal N.A., Cairo University, Faculty of Engineering, Giza, Egypt; Tounsi M., Prince Sultan University, College of Computer and Information Sciences, Riyadh, 11586, Saudi Arabia, Prince Sultan University, Automated Systems and Computing Lab (ASCL), Riyadh, Saudi Arabia; Njima C.B., University of Sousse, Tunisia
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151191-3;
Format
online
Language
English
Type
Conference paper

Citation

Danties, Anna; Nizar Banu, P.K.; Azar, Ahmad Taher; Kamal, Nashwa Ahmad; Tounsi, Mohamed; Njima, Chakib Ben, “Machine Learning and Word Representation Techniques in Medical Transcription Data Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25913.

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