Both patients and hospitals need to effectively predict wait times, whether for psychological benefits or schedule optimization needs. In this post, we will explore some of the main ways that officials predict hospital wait times and assess how successful they are at doing so.
Dynamic Time Warping (DTW) is an intelligent, dynamically adjusted metric that allows more flexibility when used in combination with any distance dependent algorithm. This flexibility allows for better classification results in many different time-series analyses.
DTW allows us to retain the temporal dynamics by directly modeling the time-series. As its names suggests, the usual Euclidean distance in problems is replaced with a dynamically adjusted metric.
The goal for this project was to determine from raw neural activity what the volunteer was thinking at any given time. This form of analysis have many direct applications, for example: prosthesis moving, computer games which interface with brain activity, and even piloting vehicles without need for physical interactions.