Evaluating Embeddings for One-Shot Classification of Doctor-AI
  Consultations
Olumide Ebenezer Ojo Olaronke Oluwayemisi Adebanji Alexander Gelbukh Hiram Calvo Anna Feldman
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.