On Monday, July 6 2020, 8:00 - 9:30, Ieva Sriubaite (Monash/CINCH) will present:
Economic Consequences of the Road Traffic Injury. Application of the Super Learner Algorithm
This paper employs methods of supervised machine learning to construct a risk adjustment tool for a set of outcomes that describes the economic consequences of the road-traffic injury. We focus on the prediction of healthcare costs and benefits from medical care in terms of both productivity as well personal well-being (the quality of life). Using the Victorian State Trauma Registry, we select all patients who experienced a major trauma in a road-traffic related accident in Victoria. To tackle statistically challenging empirical distributions we set up an ensemble machine learning algorithm - the Super Learner algorithm that is based on several parametric and non-parametric algorithms including regularized regressions, decision trees and random forests. Our findings demonstrate that the Super Learner is effective and performs best in predicting all outcomes considered in this paper.
Room: Due to the current situation regarding the COVID-19 pandemic, the talk will be held in a virtual seminar room. Please note that the talk will be held in the morning due to the difference in time zones. For more information click here.