Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes

AbstractPurpose of ReviewThe propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowing assessment of the patient disease outcome in the future.Recent FindingsIn recent years, the time series (TS) concept has become the center of attention as a predictive model for making forecast of unseen data values. TS and one of its technologies, the interrupted TS (ITS) analysis (TS with one or more interventions), predict the next period(s) value(s) of a given patient based on their past and current information. Traditional TS/ITS methods involve segmented regression-based technologies (linear and nonlinear), while stochastic (linear modeling) and artificial intelligence approaches, including machine learning (complex nonlinear relationships between variables), are also used; however, each have limitations.SummaryWe will briefly describe TS/ITS, provide examples of their application in arthritic diseases; describe their methods, challenges, and limitations; and propose a combined (stochastic and artificial intelligence) procedure in post-intervention that will optimize ITS modeling. This combined method will increase the accuracy of ITS modeling by profiting from the advantages of both stochastic and nonlinear models to capture all ITS deterministic and stochastic components. In addition, this combined method will allo...
Source: Current Rheumatology Reports - Category: Rheumatology Source Type: research