A Brief Introduction to the Temporal Group LASSO and its Potential Applications in Healthcare
It presents an incremental method for healthcare data analysis, focusing on time-series prediction tasks.
The paper introduces the Temporal Group LASSO, a multi-task regularized regression method for predicting time-varying responses, highlighting its potential in healthcare applications due to features like overfitting reduction and predictor selection.
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