Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students

摘要：

　　Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

关键词： Decision Support Systems,Clinical Formative Feedback Artificial Intelligence

作者: Piyapong Khumrin Anna Ryan Terry Judd Karin Verspoor

作者单位: Dept of Computing and Information Systems,School of Engineering,University of Melbourne,Melbourne,Au Dept of Medical Education,Melbourne Medical School,University of Melbourne,Melbourne,Australia

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 447-451

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）