交叉口(航空)
因果关系(物理学)
计算机科学
糖尿病管理
人工智能
糖尿病
机器学习
医学
数据科学
知识管理
工程类
2型糖尿病
物理
运输工程
内分泌学
量子力学
作者
Sahar Echajei,Yman Chemlal,Hanane Ferjouchia,Mostafa Rachik,Nassim Essabah Haraj,A. Chadli
出处
期刊:Synthesis lectures on engineering, science, and technology
[Morgan & Claypool]
日期:2024-01-01
卷期号:: 237-262
标识
DOI:10.1007/978-3-031-50300-9_13
摘要
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.
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