A Brief Introduction to the Temporal Group LASSO and its Potential Applications in Healthcare
Diego Saldana Miranda

TL;DR
This paper introduces the Temporal Group LASSO, a multi-task regression method for time-varying response prediction, highlighting its potential in healthcare applications due to its regularization, predictor selection, and pattern learning capabilities.
Contribution
It provides an overview of the Temporal Group LASSO method, emphasizing its advantages and potential uses in healthcare settings.
Findings
Reduces overfitting in time-varying predictions
Selects relevant predictors effectively
Learns smooth temporal effect patterns
Abstract
The Temporal Group LASSO is an example of a multi-task, regularized regression approach for the prediction of response variables that vary over time. The aim of this work is to introduce the reader to the concepts behind the Temporal Group LASSO and its related methods, as well as to the type of potential applications in a healthcare setting that the method has. We argue that the method is attractive because of its ability to reduce overfitting, select predictors, learn smooth effect patterns over time, and finally, its simplicity
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Taxonomy
TopicsMachine Learning in Healthcare · Healthcare Operations and Scheduling Optimization
