A Hybrid Approach to Sentence Classification in Clinical Data Through Word Embeddings: A Proposal

Abstract

In the biomedical field, the increased volume of unstructured data is becoming a prominent field of application for natural language processing, particularly for tasks such as sentence classification. Classifying sentences makes it possible to analyze clinical trial reports and perform clinical decision support. This study presents a framework that transforms sentences into word embedding-generated images to perform sentence classification. The proposed approach leverages the strengths of both natural language processing (NLP) and computer vision to perform robust sentence classification and offer interpretability through image processing. Furthermore, the proposed approach utilizes Class Activation Mapping (CAM) to visualize salient features within the generated images.

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