Deans' stroke musings: Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Sunday, August 27, 2023

Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

What fucking stupidity! All you are doing is predicting failure to recover! Survivors want recovery. Create protocols that will do that!

Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

Che Muhammad Nur Hidayat Che Nawi • Suhaily Mohd Hairon • Wan Nur Nafisah Wan Yahya • Wan Asyraf Wan Zaidi • Mohd Rohaizat Hassan • Kamarul Imran Musa

Published: August 26, 2023

DOI: 10.7759/cureus.44142

Peer-Reviewed

Cite this article as: Che Nawi C, Mohd Hairon S, Wan Yahya W, et al. (August 26, 2023) Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke. Cureus 15(8): e44142. doi:10.7759/cureus.44142

Abstract

The quick advancement of digital technology through artificial intelligence has made it possible to deploy machine learning to predict stroke outcomes. Our aim is to examine the trend of machine learning applications in stroke-related research over the past 50 years.

We used search terms stroke and machine learning to search for English versions of original and review articles and conference proceedings published over the past 50 years in Scopus and Web of Science databases. The Biblioshiny web application was utilized for the analysis. The trend of publication and prominent authors and journals were analyzed and identified. The collaborative network between countries was mapped, and a thematic map was used to monitor the authors' trending keywords. In total, 10,535 publications authored by 44,990 authors from 2,212 sources were retrieved. Two distinct clusters of collaborative network nodes were observed, with the United States serving as a connecting node. Three terms - deep learning, algorithms, and neural networks - are observed in the early stages of the emerging theme. Overall, international research collaborations, the establishment of global research initiatives, the development of computational science, and the availability of big data have facilitated the pervasive use of machine learning techniques in stroke research.

Introduction & Background

Globally, stroke is the main cause of death and disability. The absolute number of incident strokes increased by 70.0% (67.0-73.0), prevalent strokes increased by 85.0% (83.0-88.0), deaths from stroke increased by 43.0% (31.0-55.0), and disability-adjusted life years (DALYs) attributable to stroke increased by 32.0% (22.0-42.0) between 1990 and 2019. [1]. Besides, 63% and 80% of stroke cases happen among people less than 70 years old and have a low to moderate cardiovascular disease absolute risk, respectively. Furthermore, nearly 90% of the global stroke deaths and disability combined reside in low-to-middle-income countries [2].

Nowadays, stroke research with structured data applying machine learning (ML) algorithms for outcome prediction has gained more popularity [3]. The complexity of a condition and the availability of routinely collected datasets like stroke may lend themselves well to the application of ML methods, which can incorporate a large number of variables and observations into a single predictive framework. There were a number of studies that incorporated various machine-learning algorithms to predict stroke outcomes in various global populations [4-6]. Mortality, functional outcome, duration of hospital stay, and neurologic deterioration were the post-stroke outcomes most frequently predicted. In addition, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), and random forest (RF) were the most popular machine learning algorithms [3].

Both stroke and machine learning have been the subject of extensive research in the past. However, no investigation has been conducted into how these two distinct topics have been studied together. As the focal point of scientific evaluation, it is necessary to evaluate the growth, advancement, and impact of global research on these overlapping issues. Bibliometric analysis is a newly developed method for revealing patterns in published research. It evaluates an exhaustive literature search that demonstrates advancements within the field. Bibliometric analysis is an effective method for obtaining quantitative data on particular subjects [7]. Additionally, recognizing influential researchers and publications, countries to take into account for collaboration, and popular keywords on the application of ML in stroke research provide relevant references to the stakeholders working on stroke research to address current issues like a high stroke burden and research limitations.

In light of this, the purpose of this study was to examine the trend of machine learning applications in stroke research. Specifically, we examined its trend over the past 50 years and identified distinctive collaborative network clusters and emerging themes.