An Intelligent Literature Review: an Inductive Approach to define Machine Learning Applications in the clinical domain

Sabharwal, Renu; Miah, Shah Jahan

doi:10.21203/rs.3.rs-1090813/v1

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Disagreements were resolved by consensus. After this selection, the selected studies were submitted to LDA topic modeling from the content of abstracts, title, and keywords using keywords Knime software [ 24 ], which identifies repetitive word patterns across a corpus of documents [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Main topics in assisted reproductive market: A scoping review

Aderaldo,

Rodrigues de Albuquerque,

Câmara de Oliveira

et al. 2023

PLoS ONE

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Background Infertility affects around 12% of couples, and this proportion has been gradually increasing. In this context, the global assisted reproductive technologies (ART) market shows significant expansion, hovering around USD 26 billion in 2019 and is expected to reach USD 45 billion by 2025. Objectives We realized a scoping review of the ART market from academic publications, market reports, and specialized media news, to identify the main terms and characterize them into the main topics in the area. Design We apply an LDA topic modeling process to identify the main terms, and clustered them into semantic synonymous topics. We extracted the patterns and information to these topics and purposed a factor/consequence correlation to them. Results We found 2,232 academic papers and selected 632 to include in the automatic term detection. We also included 34 market reports and seven notices produced by specialized enterprises. Were identified 121 most relevant cited terms covering 7,806 citations. These terms were manually aggregated into 10 topics based on semantic similarity: neutral terms (37.2%), economic aspects (17.6%), in vitro fertilization (IVF) commodities & cross-border reproductive care (CBRC) (10.6%), geographic distribution (9.5%), social aspects (7%), regulation (6%), trends & concerns (3.9%), accessibility (3.4%), internet influence (2.9%), and fertility preservation for non-medical reasons (2%). Discussion The analysis indicates a market with expressive complexity. Most terms were associated with more than one topic, indicating the synergism of this market’s behavior. Only seven terms related to economic aspects, surrogacy and donation represent around 50% of the citations. Except for the topic formed by generic terms, the topic of the economic aspects was the most represented, reflecting macro perspectives such as a-la-carte standard of treatments, many clinics operating on a small/medium scale, and the recent formation of conglomerates. The IVF commodities & CBRC topic brings an overview of gametes pricing and transnational surrogacy, and its regulation. The topic of geographic distribution indicates that that the Asia-Pacific (APAC) market has the most significant growth potential in all fields. Despite the increase in supply and demand for infertility treatments and technological advances in recent decades, the success rate of IVF cycles remains at around 30%. Terms referring to research and development or technical improvement were not identified in a significant way in this review. Conclusions The formation of topics by semantic similarity proved to be an initial path for the elaboration of in-depth studies on the dynamics between several factors, for this, we present the panel classifying main terms into factors (demand, pent-up demand, or distributive) or ART market consequences. Through this approach, it was possible to observe that most of the works addresses economic aspects, regulation and geographic aspects and that topics related to research and improvement have not been addressed. In this way, we highlight the need to deepen the analysis of market elements that may be related to increased efficiency of IVF in the technical field.

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Section: Methodsmentioning

confidence: 99%

Main topics in assisted reproductive market: A scoping review

Aderaldo,

Rodrigues de Albuquerque,

Câmara de Oliveira

et al. 2023

PLoS ONE

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“…In addition to bibliometric studies, this research also uses literature review. The literature review aims to synthesize and obtain new perspectives, as well as determine the meaning and relationships between variables (Sabharwal & Miah, 2022;Kruse et al, 2022;Pablos et al, 2022).…”

Section: Research Methods General Backgroundmentioning

confidence: 99%

Top 100 Cited Research of Confirmatory Factor Analysis (CFA) in Education From 2012 to 2021

Pristianti

2022

Int. J. Curr. Educ. Res.

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The aim of this research is to analyze the trends and future research opportunities related to confirmatory factor analysis in education. This research uses a descriptive bibliometric analysis and literature review to determine research trends. The data in this research used the Scopus database during 2012-2021. Data in form .CSV was analyzed using Ms. Excel and data in form .RIS was analyzed using VOSviewer. The conclusion from this research are the trend of research on this topic has increased every year in the last ten years. The most widely used keyword is confirmatory factor analysis. Research related to CFA in education has a wide and good opportunity to be carried out for several reasons including: (1) the trend shows a good graph and continues to increase; (2) the number of citations per paper per year shows a number with a high average; (3) From many studies, it is stated that CFA has a contribution in educational research. For future research, we can focus on discussing CFA in a multidisciplinary manner or discussing CFA trends in education using databases other than Scopus.

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“…Recent studies have focused on the proper selection of EEG signal characteristics and its effect on the accuracy of ML and DL algorithms, as presented in [30]. ML and DL techniques are widely accepted and help to develop specific tasks within different applications [55][56][57][58][59][60][61]. Moreover, they are increasingly used to obtain EEG data for pattern analysis, classification of group membership, and BCIs [29,[62][63][64][65][66][67].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

et al. 2022

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In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

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An Intelligent Literature Review: an Inductive Approach to define Machine Learning Applications in the clinical domain

Cited by 3 publications

References 21 publications

Main topics in assisted reproductive market: A scoping review

Main topics in assisted reproductive market: A scoping review

Top 100 Cited Research of Confirmatory Factor Analysis (CFA) in Education From 2012 to 2021

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

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