2018 AANS Annual Scientific Meeting-Computational Modeling to Predict Functional Neurosurgery Outcomes

Computational Modeling to Predict Functional Neurosurgery Outcomes

Video Transcription

Video Summary

In this video transcript, Dr. Mark Richardson discusses computational modeling to predict functional neurosurgery outcomes. He explains that computational modeling is a combination of techniques from math, physics, and computer science used to simulate the behavior of complex systems. He acknowledges two individuals, Nathan Sisserson and Tom Wozni, who have helped educate him on this topic. Dr. Richardson explains the steps involved in modeling outcomes, including identifying neurophysiologic variables, building a model for physiology, identifying therapeutic variables, and simulating all possible combinations to predict outcomes. He also discusses the importance of modeling at different scales and the challenges of data complexity and data extrapolation. Dr. Richardson provides examples from the epilepsy and deep brain stimulation (DBS) literature to illustrate how computational modeling is being used to predict surgery outcomes and inform clinical decisions. He concludes by emphasizing the need for data organization, data warehouses, and standardization of data formats. The summary is under 150 words.

Asset Caption

Robert Mark Richardson, MD, PhD, FAANS

Keywords

computational modeling

neurosurgery outcomes

physiology model

data complexity

clinical decisions