论文标题

建筑信任:来自Technion-Rambam机器学习的教训

Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event

论文作者

Sobel, Jonathan A., Almog, Ronit, Celi, Leo Anthony, Gaziel-Yablowitz, Michal, Eytan, Danny, Behar, Joachim A.

论文摘要

Datathon是一项涉及应用于特定问题的数据科学的时间限制的竞争。在过去的十年中,DATATHON已被证明是领域和专业知识之间的宝贵桥梁。生物医学数据分析是一个具有挑战性的领域,需要工程师,生物学家和医生之间的合作,以更好地了解患者生理学以及指导诊断,预后和治疗干预措施以改善护理实践的决策过程。在这里,我们反思了我们在2022年3月底在MIT关键数据组,Rambam Health Care Campus(Rambam)和Haifa技术以色列技术研究所(Technion Institute of Haifa)组织的活动的结果。要求参与者完成有关他们的技能和兴趣的调查,这使我们能够确定机器学习培训对医疗问题应用的当前需求。这项工作描述了以色列背景下医学数据科学的机会和局限性。

A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fields and expertise . Biomedical data analysis represents a challenging area requiring collaboration between engineers, biologists and physicians to gain a better understanding of patient physiology and of guide decision processes for diagnosis, prognosis and therapeutic interventions to improve care practice. Here, we reflect on the outcomes of an event that we organized in Israel at the end of March 2022 between the MIT Critical Data group, Rambam Health Care Campus (Rambam) and the Technion Israel Institute of Technology (Technion) in Haifa. Participants were asked to complete a survey about their skills and interests, which enabled us to identify current needs in machine learning training for medical problem applications. This work describes opportunities and limitations in medical data science in the Israeli context.

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