A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics

Kumari, Juli; Kumar, Ela; Kumar, Deepak

doi:10.1007/s11831-023-09915-y

Cited by 25 publications

(7 citation statements)

References 54 publications

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“…This agreement aligns with previous research that highlights the exponential growth in scientific output due to technological advancements, underscoring the increasing interest and commitment to this field of study. Moreover, the prevalence of leading nations in terms of research output, citations, and collaborations demonstrates established connections between research productivity and impact as evidenced by bibliometric analysis (Kumari et al, 2023). These findings, in line with prior research, emphasize the pivotal roles of leading nations in shaping scientific discussions and enhancing specific subjects.…”

Section: Discussionsupporting

confidence: 72%

Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research

Shafi,

Reshi,

Shah

et al. 2024

IJERR

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The present bibliometric analysis investigates the scholarly output from 2013 to 2022 to explore the use of artificial intelligence (AI) in Down syndrome research. The analysis demonstrates a significant and rapid growth in publications, starting from a minimal number of 17 articles in 2013 and reaching a peak of 2162 by 2022. This indicates a notable increase in interest and dedication to this topic. Upon analyzing national contributions, the United States stands up as a frontrunner in terms of research output, citations, and collaborations. This underscores its crucial role in influencing the discussion and influence of AI-driven Down syndrome research. The dynamics of collaboration, especially between the United States and countries like the United Kingdom, China, and Germany, illustrate a vast worldwide network that facilitates the exchange of information. The congruence of these findings with past research highlights the regularity in exponential growth tendencies, which can be attributed to technical discoveries and interdisciplinary cooperation. Moreover, the prevalence of dominant nations, the significance of renowned publications, and the sway of prolific authors underscore the firmly established connections between research productivity and influence within specific fields of study. The study's findings suggest that future developments in AI-driven Down syndrome research will focus on integrating AI more deeply, fostering interdisciplinary collaborations, and prioritizing ethical considerations. These trends align with the anticipated paths and ethical obligations in this field. Keywords: Artificial intelligence; Bibliometric Analysis; Down syndrome

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Section: Discussionsupporting

confidence: 72%

Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research

Shafi,

Reshi,

Shah

et al. 2024

IJERR

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“…This behavior, which is also observed in the widespread use of ML and DL techniques in diabetes research (Figure 2), is consistent with recent research that reports a notable increase in studies on AI techniques and their applications in areas of health sciences since 2018. [68][69][70] Meanwhile, from 2019 to the present, new more relevant terms have been emerging focused on the prediction, classification and diagnosis of diabetes using established machine learning techniques such as support vector machines. However, since 2020, there has been a strong emphasis on an approach that includes deep learning techniques such as convolutional neural networks, applied mainly to the detection and classification of eye diseases related to complications such as retinopathy.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

García-Jaramillo,

Luque,

León-Vargas

2023

J Diabetes Sci Technol

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Background: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain. Methods: The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data. Results: A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production. Conclusion: This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

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“…In recent years, with the increasing availability of medical data and the continuous improvement in computer analysis capabilities, machine learning has been increasingly used in the medical field [ 23 , 24 ]. Machine learning is a technological application that uses algorithms and data to enable computers to automatically learn and enhance.…”

Section: Introductionmentioning

confidence: 99%

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Wang,

Chang,

Liu

et al. 2024

J Med Internet Res

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Background Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. Objective This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. Results A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. Conclusions Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

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A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics

Cited by 25 publications

References 54 publications

Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research

Artificial Intelligence Driven Bibliometric Insights: Pioneering Down Syndrome Research

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

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