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- W2978114316 abstract "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%." @default.
- W2978114316 created "2019-10-10" @default.
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- W2978114316 date "2019-07-01" @default.
- W2978114316 modified "2023-09-27" @default.
- W2978114316 title "Towards A Deep Learning Question-Answering Specialized Chatbot for Objective Structured Clinical Examinations" @default.
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- W2978114316 doi "https://doi.org/10.1109/ijcnn.2019.8851729" @default.
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