Predicting Type 2 Diabetes Complications and Personalising Patient Using Artificial Intelligence Methodology

Abstract

The prediction of the onset of different complications of disease, in general, is challenging due to the existence of unmeasured risk factors, imbalanced data, time-varying data due to dynamics, and various interventions to the disease over time. Scholars share a common argument that many Artificial Intelligence techniques that successfully model disease 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. There is a need for appropriate Artificial Intelligence techniques to build predictive models that not only capture unmeasured effects to improve prediction, but are also transparent in how they model data so that knowledge about disease processes can be extracted and trust in the model can be maintained by clinicians. The proposed strategy builds probabilistic graphical models for prediction with the inclusion of informative hidden variables. These are added in a stepwise manner to improve predictive performance whilst maintaining as simple a model as possible, which is regarded as crucial for the interpretation of the prediction results. This chapter explores this key issue with a specific focus on diabetes data. According to the literature on disease modelling, especially on major diseases such as diabetes, a patient’s mortality often occurs due to the associated complications caused by the disease over time and not the disease itself. This is often patient-specific and will depend on what type of cohort a patient belongs to. Another main focus of this study is patient personalisation via precision medicine by discovering meaningful subgroups of patients which are characterised as phenotypes. These phenotypes are explained further using Bayesian network analysis methods and temporal association rules. Overall, this chapter discussed the earlier research of the chapter’s author. It explores Artificial Intelligence (IDA) techniques for modelling the progression of disease whilst simultaneously stratifying patients and doing so in a transparent manner as possible. To this end, it reviews the current literature on some of the most common Artificial Intelligent (AI) methodologies, including probabilistic modelling, association rule mining, phenotype discovery and latent variable discovery by using diabetes as a case study.

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

“…AI plays an important role in predicting complications using basic clinical and biochemical patient data, but predicting the occurrence of different complications is a challenging task due to different risk factors, unbalanced data and rapid changes (Singla et al, 2019). Therefore, there is an increasing emphasis on the use of appropriate AI techniques to predict prognosis (Singla et al, 2019;Yousefi & Tucker, 2020). Consequently, accurate prediction helps to target nursing interventions better (Ljubic et al, 2020) Nurses, as the largest part of all workers involved in health systems, will benefit enormously from AI (Shang, 2021).…”

“…The mentioned state describes several concomitant conditions with a significant impact on patient care and life quality (Chima et al, 2017 ). Predicting the development of disease complications is a demanding process due to the existence of unmeasured risk factors, unbalanced data, time‐varying dynamics data and various interventions for the disease (Yousefi & Tucker, 2020 ).…”