Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations

Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generated texts in healthcare consultations can be classified using state-of-the-art embeddings and one-shot classification syst...

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Bibliographic Details
Published inarXiv.org
Main Authors Olumide Ebenezer Ojo, Adebanji, Olaronke Oluwayemisi, Gelbukh, Alexander, Calvo, Hiram, Feldman, Anna
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.02.2024
Subjects
Classification
Health care
Machine learning
Patients
Physicians
Semantics
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Summary:Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generated texts in healthcare consultations can be classified using state-of-the-art embeddings and one-shot classification systems. By analyzing embeddings such as bag-of-words, character n-grams, Word2Vec, GloVe, fastText, and GPT2 embeddings, we examine how well our one-shot classification systems capture semantic information within medical consultations. Results show that the embeddings are capable of capturing semantic features from text in a reliable and adaptable manner. Overall, Word2Vec, GloVe and Character n-grams embeddings performed well, indicating their suitability for modeling targeted to this task. GPT2 embedding also shows notable performance, indicating its suitability for models tailored to this task as well. Our machine learning architectures significantly improved the quality of health conversations when training data are scarce, improving communication between patients and healthcare providers.
ISSN:2331-8422