A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

Abstract Background Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. Methods The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. Results The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. Conclusion ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

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Power-Enhanced Funnel Plots for Meta-Analysis

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000392 ◽

2020 ◽

Vol 228 (1) ◽

pp. 43-49 ◽

Cited By ~ 1

Author(s):

Michael Kossmeier ◽

Ulrich S. Tran ◽

Martin Voracek

Keyword(s):

Statistical Power ◽

Meta Analysis ◽

Relevant Information ◽

Selective Reporting ◽

Graphical Display ◽

Funnel Plot ◽

Evidential Value ◽

Graphical Displays ◽

Funnel Plots ◽

Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

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389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽

10.2337/db20-389-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 389-P

Author(s):

SATORU KODAMA ◽

MAYUKO H. YAMADA ◽

YUTA YAGUCHI ◽

MASARU KITAZAWA ◽

MASANORI KANEKO ◽

...

Keyword(s):

Machine Learning ◽

Meta Analysis ◽

Machine Learning Techniques ◽

Learning Techniques

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An Epistemological Criticism of Social Entrepreneurship: Is Social Entrepreneurship a Sound Scientific Field of Research

10.31124/advance.7091927.v1 ◽

2018 ◽

Author(s):

Amir Forouharfar

Keyword(s):

Social Entrepreneurship ◽

Social Constructivism ◽

Social Epistemology ◽

American English ◽

Meta Analysis ◽

Research Question ◽

Knowledge Structure ◽

Scientific Field ◽

The Other ◽

First Time

The paper was shaped around the pivotal question: Is SE a sound and scientific field of research? The question has given a critical tone to the paper and has also helped to bring out some of the controversial debates in the realm of SE. The paper was organized under five main discussions to be able to provide a scientific answer to the research question: (1) is “social entrepreneurship” an oxymoron?, (2) the characteristics of SE knowledge, (3) sources of social entrepreneurship knowledge, (4) SE knowledge: structure and limitations and (5) contributing epistemology-making concepts for SE. Based on the sections, the study relied on the relevant philosophical schools of thought in Epistemology (e.g. Empiricism, Rationalism, Skepticism, Internalism vs. Externalism, Essentialism, Social Constructivism, Social Epistemology, etc.) to discuss these controversies around SE and proposes some solutions by reviewing SE literature. Also, to determine the governing linguistic discourse in the realm of SE, which was necessary for our discussion, Corpus of Contemporary American English (COCA) for the first time in SE studies was used. Further, through the study, SE buzzwords which constitute SE terminology were derived and introduced to help us narrowing down and converging the thoughts in this field and demarking the epistemological boundaries of SE. The originality of the paper on one hand lies in its pioneering discussions on SE epistemology and on the other hand in paving the way for a construction of sound epistemology for SE; therefore in many cases after preparing the philosophical ground for the discussions, it went beyond the prevalent SE literature through meta-analysis to discuss the cases which were raised. The results of the study verified previously claimed embryonic pre-paradigmatic phase in SE which was far from a sound and scientific knowledge, although the scholarly endeavors are the harbingers of such a possibility in the future which calls for further mature academic discussion and development of SE knowledge by the SE academia.

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽

10.3390/electronics10131550 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1550

Author(s):

Alexandros Liapis ◽

Evanthia Faliagka ◽

Christos P. Antonopoulos ◽

Georgios Keramidas ◽

Nikolaos Voros

Keyword(s):

Machine Learning ◽

Deep Learning ◽

User Experience ◽

Electrodermal Activity ◽

Binary Classification ◽

Research Question ◽

Classification Problem ◽

Categorical Variables ◽

Stress Detection ◽

Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

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Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

Global Business Review ◽

10.1177/0972150920984857 ◽

2021 ◽

pp. 097215092098485

Author(s):

Sonika Gupta ◽

Sushil Kumar Mehta

Keyword(s):

Machine Learning ◽

Data Mining ◽

Literature Review ◽

Systematic Literature Review ◽

Classification Accuracy ◽

Meta Analysis ◽

Financial Statement ◽

Research Articles ◽

Financial Statement Fraud ◽

Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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Inter-Experiment Machine Learning on APT experiments: New Insights from Meta-Analysis

Microscopy and Microanalysis ◽

10.1017/s1431927621001264 ◽

2021 ◽

Vol 27 (S1) ◽

pp. 182-183

Author(s):

Martin Meier ◽

Paul Bagot ◽

Michael Moody ◽

Daniel Haley

Keyword(s):

Machine Learning ◽

Meta Analysis

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Mass drug administration campaigns for scabies and impetigo: protocol for a systematic review and meta-analysis

BMJ Paediatrics Open ◽

10.1136/bmjpo-2021-001132 ◽

2021 ◽

Vol 5 (1) ◽

pp. e001132

Author(s):

Pousali Ghosh ◽

Wubshet Tesfaye ◽

Avilasha Manandhar ◽

Thomas Calma ◽

Mary Bushell ◽

...

Keyword(s):

Systematic Review ◽

Drug Administration ◽

Mass Drug Administration ◽

Assessment Tool ◽

Statistical Tests ◽

Meta Analysis ◽

Grey Literature ◽

Random Effect ◽

Systematic Evaluation ◽

Clear Understanding

IntroductionScabies is recognised as a neglected tropical disease, disproportionately affecting the most vulnerable populations around the world. Impetigo often occurs secondarily to scabies. Several studies have explored mass drug administration (MDA) programmes, with some showing positive outcomes—but a systematic evaluation of such studies is yet to be reported. The main aim of this systematic review is to generate comprehensive evidence on the effect and feasibility of MDA programmes in reducing the burden of scabies and impetigo.Methods and analysisA systematic review and meta-analysis will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement. Electronic databases to be searched will include CINAHL EBSCOhost, Medline Ovid, ProQuest, Science Direct, PubMed and SCOPUS. In addition, grey literature will be explored via the Australian Institute of Health and Welfare, Australian Indigenous HealthInfoNet, Informit, OaIster database and WHO. No language restrictions will be applied. All treatment studies following an MDA protocol, including randomised/quasi-controlled trials, and prospective before–after interventional studies, will be considered. The main outcome is the change in prevalence of scabies and impetigo The Cochrane collaboration risk of bias assessment tool will be used for assessing the methodological quality of studies. A random-effect restricted maximum likelihood meta-analysis will be performed to generate pooled effect (OR) using STATA V.16. Appropriate statistical tests will be carried out to quantify heterogeneity between studies and publication bias.Ethics and disseminationEthical approval is not required since data will be extracted from published works. The findings will be communicated to the scientific community through a peer-reviewed journal publication. This systematic review will present an evidence on the effect of MDA interventions on scabies and impetigo, which is instrumental to obtain a clear understanding of the treatments widely used in these programmes.PROSPERO registration numberCRD42020169544,

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Study protocol of a systematic review and qualitative evidence synthesis using two different approaches: Healthcare related needs and desires of older people with post-stroke aphasia

BMJ Open ◽

10.1136/bmjopen-2020-039348 ◽

2021 ◽

Vol 11 (4) ◽

pp. e039348

Author(s):

Nadine Janis Pohontsch ◽

Thorsten Meyer ◽

Yvonne Eisenmann ◽

Maria-Inti Metzendorf ◽

Verena Leve ◽

...

Keyword(s):

Systematic Review ◽

Older People ◽

Evidence Synthesis ◽

Meta Analysis ◽

Research Question ◽

Treatment Success ◽

Qualitative Studies ◽

Qualitative Evidence Synthesis ◽

Healthcare Needs ◽

Qualitative Evidence

IntroductionStroke is a frequent disease in the older population of Western Europe with aphasia as a common consequence. Aphasia is known to impede targeting treatment to individual patients’ needs and therefore may reduce treatment success. In Germany, the postacute care of patients who had stroke is provided by different healthcare institutions of different sectors (rehabilitation, nursing and primary care) with substantial difficulties to coordinate services. We will conduct two qualitative evidence syntheses (QESs) aiming at exploring distinct healthcare needs and desires of older people living with poststroke aphasia. We thereby hope to support the development of integrated care models based on needs of patients who are very restricted to communicate them. Since various methods of QESs exist, the aim of the study embedding the two QESs was to determine if findings differ according to the approach used.Methods and analysisWe will conduct two QESs by using metaethnography (ME) and thematic synthesis (ThS) independently to synthesise the findings of primary qualitative studies. The main differences between these two methods are the underlying epistemologies (idealism (ME) vs realism (ThS)) and the type of research question (emerging (ME) vs fixed (ThS)).We will search seven bibliographical databases. Inclusion criteria comprise: patients with poststroke aphasia, aged 65 years and older, studies in German/English, all types of qualitative studies concerning needs and desires related to healthcare or the healthcare system. The protocol was registered in the International Prospective Register of Systematic Reviews, follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines and includes three items from the Enhancing Transparency in Reporting the synthesis of Qualitative Research checklist.Ethics and disseminationEthical approval is not required. Findings will be published in a peer-reviewed journal and presented on national conferences.

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Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes

Scientific Reports ◽

10.1038/s41598-021-82482-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Balamurugan Sadaiappan ◽

Chinnamani PrasannaKumar ◽

V. Uthara Nambiar ◽

Mahendran Subramanian ◽

Manguesh U. Gauns

Keyword(s):

Machine Learning ◽

Iron Transport ◽

Meta Analysis ◽

Zooplankton Community ◽

Gradient Boosting ◽

Learning Approaches ◽

16S Rdna Gene ◽

Marine Copepod ◽

Form Of Life ◽

First Time

AbstractCopepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles. For this, machine-learning models and PICRUSt2 analysis were deployed to analyse 16S rDNA gene sequences (approximately 16 million reads) of CAB belonging to five different copepod genera viz., Acartia spp., Calanus spp., Centropages sp., Pleuromamma spp., and Temora spp.. Overall, we predict 50 sub-OTUs (s-OTUs) (gradient boosting classifiers) to be important in five copepod genera. Among these, 15 s-OTUs were predicted to be important in Calanus spp. and 20 s-OTUs as important in Pleuromamma spp.. Four bacterial s-OTUs Acinetobacter johnsonii, Phaeobacter, Vibrio shilonii and Piscirickettsiaceae were identified as important s-OTUs in Calanus spp., and the s-OTUs Marinobacter, Alteromonas, Desulfovibrio, Limnobacter, Sphingomonas, Methyloversatilis, Enhydrobacter and Coriobacteriaceae were predicted as important s-OTUs in Pleuromamma spp., for the first time. Our meta-analysis revealed that the CAB of Pleuromamma spp. had a high proportion of potential genes responsible for methanogenesis and nitrogen fixation, whereas the CAB of Temora spp. had a high proportion of potential genes involved in assimilatory sulphate reduction, and cyanocobalamin synthesis. The CAB of Pleuromamma spp. and Temora spp. have potential genes accountable for iron transport.

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