A dataset of simulated patient-physician medical interviews with a focus on respiratory cases

A dataset of simulated patient-physician medical interviews with a focus on respiratory cases

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Abstract Artificial Intelligence (AI) is playing a major role in medical education, diagnosis, and outbreak detection through Natural Language Processing (NLP), machine learning models and deep learning tools.However, graham c+ cream in order to train AI to facilitate these medical fields, well-documented and accurate medical conversations are needed.The dataset presented covers a series of medical conversations in the format of Objective Structured Clinical Examinations (OSCE), with a focus on respiratory cases in audio format and corresponding text documents.These cases were simulated, recorded, transcribed, and manually corrected with the underlying aim of providing ashley furniture porter b697-92 (porter 3-drawer nightstand) a comprehensive set of medical conversation data to the academic and industry community.

Potential applications include speech recognition detection for speech-to-text errors, training NLP models to extract symptoms, detecting diseases, or for educational purposes, including training an avatar to converse with healthcare professional students as a standardized patient during clinical examinations.The application opportunities for the presented dataset are vast, given that this calibre of data is difficult to access and costly to develop.