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.
Dokumententyp: | Paper |
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Keywords: | Applications (stat.AP); FOS: Computer and information sciences; FOS: Computer and information sciences; Artificial Intelligence; AI, Künstliche Intelligenz; KI |
Fakultät: | Betriebswirtschaft > Institute of Artificial Intelligence (AI) in Management |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme |
Sprache: | Englisch |
Dokumenten ID: | 94978 |
Datum der Veröffentlichung auf Open Access LMU: | 10. Mrz. 2023, 07:39 |
Letzte Änderungen: | 10. Mrz. 2023, 07:43 |