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Medical students undergo exams, called "Objective Structured Clinical Examinations" (OSCEs), to assess their medical competence in clinical tasks. In these OSCEs, a medical student interacts with a standardized patient, asking questions to complete a clinical assessment of the patient's medical case. In real OSCEs, standardized patients or "Actors" are recruited and trained to answer questions about symptoms mentioned in a script designed by the medical examiner. Developing a virtual conversational patient for OSCEs would lead to significant logistical savings. In this work, we develop a deep learning framework to improve the virtual patient's conversational skills. First, deep neural networks learned domain specific word embeddings. Then, long short-term memory networks derived sentence embeddings before a convolutional neural network model selected an answer to a given question from a script. Empirical results on a homegrown corpus showed that this framework outperformed other approaches, and reached an accuracy of 81%.
Zini et al. (Mon,) studied this question.