Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules. (29th March 2020)

• Record Type: Journal Article
• Authors: Yousefi, Leila; Swift, Stephen; Arzoky, Mahir; Saachi, Lucia; Chiovato, Luca; Tucker, Allan
• Other Names: Ventura Sebastian guestEditor.; Soda Paolo guestEditor.; González Alejandro Rodríguez guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 4(2021)
• Page Start: 1460
• Page End: 1498
• Publication Date: 2020-03-29
• Subjects: diabetes associated complication rules -- latent variable discovery -- patient personalization -- temporal phenotype -- time series clustering; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12313
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a "black box" where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a "latent phenotype, " with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data.