BIOS-06. MACHINE LEARNING MADE EASY: DESIGN OF A PROPOSAL FOR A MEDICAL STUDENT EDUCATIONAL SESSION ON HIGH YIELD STRATEGIES FOR LEARNING AND APPLYING DATA SCIENCE

Abstract

Abstract Machine learning is a burgeoning field in data science that has many applications in medicine, and it is especially useful in neurosurgery due to the often-complex and potentially emergent nature of day-to-day cases. Machine learning models have the potential to deliver accurate patient prognostication while offering the benefit of providing real-time AI supported clinical decision-making with high data granularity. These models can utilize numerical inputs for more straightforward applications and, with practice, can enable clinicians and trainees to incorporate advanced imaging sequences as inputs to optimize the accuracy of patient prognostication. In the future, these models will become much more commonplace in the clinical setting for neurosurgeons. As medical school curricula do not typically incorporate these statistical techniques into biomedical statistics courses, symposia centered on machine learning could prove essential to medical students seeking to learn these skills. This session would include a brief overview of machine learning models and the procedures for using the programming language of RStudio for the development of novel internet-based prognostication calculators. The benefit of RStudio is that it is user friendly and pre-designed templates are available that can enable students to perform facile customization in tailoring the code to the needs of their clinical investigation. There will be a brief overview of accuracy verification involving discussion of receiver operating curves with the use of training and testing datasets - the heart of machine learning. We have designed and outlined a curriculum that will enable students to identify and classify different techniques (support vector machines, logistic regression, clustering, nearest neighbor classifiers), and recognize the utility of machine learning approaches to neurosurgical outcomes prognostication. Our hope is that students will leave this symposium well-equipped with a newfound confidence and the baseline conceptual framework needed to begin exploring the awesome world of machine learning throughout their training.

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