Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914669898235904 |
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| author | Ojo, Olumide Ebenezer Adebanji, Olaronke Oluwayemisi Gelbukh, Alexander Calvo, Hiram Feldman, Anna |
| author_facet | Ojo, Olumide Ebenezer Adebanji, Olaronke Oluwayemisi Gelbukh, Alexander Calvo, Hiram Feldman, Anna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04442 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations Ojo, Olumide Ebenezer Adebanji, Olaronke Oluwayemisi Gelbukh, Alexander Calvo, Hiram Feldman, Anna Computation and Language 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. |
| title | Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.04442 |