Classification of specialities in textual medical reports based on natural language processing and feature selection

Abstract

Nowadays, a great deal of detailed information about patients, including disease status, medication history, and side effects, is collected in an electronic format; called an electronic medical record (EMR), and the data serves as a valuable resource for further analysis, diagnosis, and treatment. The huge q uantity of detailed patient information in these medical texts produces a huge challenge in terms of processing this data efficiently, however. Machine learning (ML) algorithms, artificial intelligence techniques, and natural language processing tools can have the potential effect of simplifying unstructured data, which could positively affect medical report analysis. Natural language processing (NLP) has recently made huge advances on a variety of tasks. In this paper, an automatic system was thus produced to classify specialist consultant interactions based on patients’ medical reports. NLP was used as a pre-processing step on a dataset formed of unstructured medical reports. Feature extraction and selection methods were used to convert the textual reports into sets of features and to extract the most effective features to increase classification accuracy and reduce execution time. Various classification methods were then applied (ML perceptron, logistic regression random forest (RF), and linear support vec tor classifier (LSVC)). The highest accuracy (99.39%) was achieved in ML-perceptron classification techniques .

The potential of ChatGPT technology in education: advantages, obstacles and future growth

“…-Strengthening deep learning and active engagement: to achieve improved knowledge dissemination and more accurate contextual understanding, ChatGPT is poised to develop more sophisticated text analytics and engagement. -Maximising visual and audio knowledge: ChatGPT may be able to provide high-engagement experiences by merging linguistic information with visual and audio knowledge [32], [40], [41].…”