Exploring the Intersection of Machine Learning and Causality in Advanced Diabetes Management: New Insight and Opportunities

In light of the exponential surge in extensive quantities of medical data and the intrinsic uncertainty it engenders, numerous data specialists and epidemiologists have proposed various approaches to analyze causal effects from observational data, bridging the gap between health science and data analysis. The present emphasis on enhancing diabetes prevention and managing its complications primarily arises from two key factors: (i) the escalating occurrence of diabetes and (ii) significant advancements in clinical inquiries, specifically observational investigations, facilitated by the increasing accessibility of Real-World Evidence. This paper aims to synthesize the discoveries derived from a multitude of meticulously selected research papers that delve into the application of Machine Learning and Causal Inference methodologies within the healthcare domain, with a distinct concentration on diabetology. The objective is to address inquiries pertaining to cause-and-effect relationships. This will serve as the fundamental basis for constructing a causal system to forecast the optimal sequence of pharmaceuticals to be administered to a patient and effectively manage the process of drug dosage planning. Machine Learning helps understand intervention impacts in complex causal landscapes with diverse effects, aiding decision-makers with valuable approximations of alterations and variables.