Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Abstract

With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

Imagining the Future of Learning in Healthcare: The GAME 2019 #FuturistForum

“…nuclear cardiology, pathology, radiology) [30]. In parallel, research will continue to be conducted on the multiple potential uses of machine learning in medicine [31][32][33].…”

Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics

“…Recent advancements in artificial intelligence and machine learning (ML) have shown potential for early disease detection and risk prediction. 17 ML enables the recognition of complex patterns in data to predict outcomes without being limited by established risk factors. By applying ML models to traditional indices as well as emerging indices derived from coronary angiography, this study aims to overcome CMR challenges in patients with STEMI for the early and practical prediction of LVR risk.…”

Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis

“…Although object prediction systems, such as ASTRAL (Michel et al, 2010), DRAGON (Wang et al, 2017), and THRIVE (Flint et al, 2014), have been reported to assess the efficiency of intravenous thrombolysis in ischemic stroke patients, most of these scales are based on traditional algorithms with limited clinical features. With recent developments in artificial intelligence, medical machine learning has produced several exciting findings (Jamin et al, 2021;Jayatilake and Ganegoda, 2021). Considering its extended impact on ischemic stroke management, machine learning (ML) models for outcome prediction in patients with intravenous thrombolysis were developed based on the comparison of clinical data according to the modified Rankin score (mRs) at 90 days after thrombolysis.…”