Data-driven dynamic treatment planning for chronic diseases

TL;DR

This paper introduces a novel data-driven model, VDC-HMMX, for identifying disease trajectory phases in chronic conditions, improving treatment planning accuracy through longitudinal data analysis.

Contribution

The paper presents a new variable-duration copula hidden Markov model (VDC-HMMX) for better phase detection in chronic disease management, outperforming traditional classifiers.

Findings

VDC-HMMX achieves 83.65% balanced accuracy in phase identification.

Longitudinal monitoring significantly improves treatment regimen accuracy.

Model effectively recovers latent disease phases from patient data.

Abstract

In order to deliver effective care, health management must consider the distinctive trajectories of chronic diseases. These diseases recurrently undergo acute, unstable, and stable phases, each of which requires a different treatment regimen. However, the correct identification of trajectory phases, and thus treatment regimens, is challenging. In this paper, we propose a data-driven, dynamic approach for identifying trajectory phases of chronic diseases and thus suggesting treatment regimens. Specifically, we develop a novel variable-duration copula hidden Markov model (VDC-HMMX). In our VDC-HMMX, the trajectory is modeled as a series of latent states with acute, stable, and unstable phases, which are eventually recovered. We demonstrate the effectiveness of our VDC-HMMX model on the basis of a longitudinal study with 928 patients suffering from low back pain. A myopic classifier identifies correct treatment regimens with a balanced accuracy of slightly above 70%. In comparison, our VDC-HMMX model is correct with a balanced accuracy of 83.65%. This thus highlights the value of longitudinal monitoring for chronic disease management.