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Book ; Online: Multi-document Summarization

Hewapathirana, Kushan / de Silva, Nisansa / Athuraliya, C. D.

A Comparative Evaluation

2023

Abstract: This paper is aimed at evaluating state-of-the-art models for Multi-document Summarization (MDS) on different types of datasets in various domains and investigating the limitations of existing models to determine future research directions. To address ... ...

Abstract	This paper is aimed at evaluating state-of-the-art models for Multi-document Summarization (MDS) on different types of datasets in various domains and investigating the limitations of existing models to determine future research directions. To address this gap, we conducted an extensive literature review to identify state-of-the-art models and datasets. We analyzed the performance of PRIMERA and PEGASUS models on BigSurvey-MDS and MS$^2$ datasets, which posed unique challenges due to their varied domains. Our findings show that the General-Purpose Pre-trained Model LED outperforms PRIMERA and PEGASUS on the MS$^2$ dataset. We used the ROUGE score as a performance metric to evaluate the identified models on different datasets. Our study provides valuable insights into the models' strengths and weaknesses, as well as their applicability in different domains. This work serves as a reference for future MDS research and contributes to the development of accurate and robust models which can be utilized on demanding datasets with academically and/or scientifically complex data as well as generalized, relatively simple datasets.
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-09-10
Publishing country	us
Document type	Book ; Online
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Article: A data-driven biocomputing pipeline with meta-analysis on high throughput transcriptomics to identify genome-wide miRNA markers associated with type 2 diabetes.

De Silva, Kushan / Demmer, Ryan T / Jönsson, Daniel / Mousa, Aya / Forbes, Andrew / Enticott, Joanne

Heliyon

2022 Volume 8, Issue 2, Page(s) e08886

Abstract: ... expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three ... meta-analytic algorithms (robust rank aggregation; Fisher's method of : Results: A total of 1256 DE-miRNAs ... of DE-miRNAs associated with T2D was discovered via in-silico analyses and its pathobiological relevance ...

Abstract	Background: MicroRNAs (miRNAs) are sought-after biomarkers of complex, polygenic diseases such as type 2 diabetes (T2D). Data-driven biocomputing provides robust and novel avenues for synthesizing evidence from individual miRNA seq studies. Objective: To identify miRNA markers associated with T2D, via a data-driven, biocomputing approach on high throughput transcriptomics. Materials and methods: The pipeline consisted of five sequential steps using miRNA seq data retrieved from the National Center for Biotechnology Information Gene Expression Omnibus platform: systematic review; identification of differentially expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three normalization algorithms (trimmed mean of M-values; upper quartile; relative log expression) and two meta-analytic algorithms (robust rank aggregation; Fisher's method of Results: A total of 1256 DE-miRNAs (821 downregulated; 435 upregulated) were identified from 5 eligible miRNA seq datasets (3 circulatory; 1 adipose; 1 pancreatic). The meta-signature comprised 9 miRNAs ( Conclusions: A meta-signature of DE-miRNAs associated with T2D was discovered via in-silico analyses and its pathobiological relevance was validated against corroboratory evidence from contemporary studies and downstream analyses. The miRNA meta-signature could be useful for guiding future studies on T2D. There may also be avenues for using the pipeline more broadly for evidence synthesis on other conditions using high throughput transcriptomics.
Language	English
Publishing date	2022-02-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e08886
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Article ; Online: Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model.

Belsti, Yitayeh / Moran, Lisa / Du, Lan / Mousa, Aya / De Silva, Kushan / Enticott, Joanne / Teede, Helena

International journal of medical informatics

2023 Volume 179, Page(s) 105228

Abstract: Background: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability ... ...

Abstract	Background: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal. Objective: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM. Methods: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed. Results: Upon internal validation, the machine learning and logistic regression model's area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39). Conclusions: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.
Language	English
Publishing date	2023-09-21
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2023.105228
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Article ; Online: Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow.

De Silva, Kushan / Demmer, Ryan T / Jönsson, Daniel / Mousa, Aya / Forbes, Andrew / Enticott, Joanne

Functional & integrative genomics

2022 Volume 22, Issue 5, Page(s) 1003–1029

Abstract: Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes ... ...

Abstract	Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.
MeSH term(s)	COVID-19 ; Computational Biology ; Diabetes Mellitus, Type 2/genetics ; Humans ; Insulin ; Meta-Analysis as Topic ; Systematic Reviews as Topic ; Workflow
Chemical Substances	Insulin
Language	English
Publishing date	2022-07-05
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2014670-X
ISSN	1438-7948 ; 1438-793X
ISSN (online)	1438-7948
ISSN	1438-793X
DOI	10.1007/s10142-022-00881-5
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Article ; Online: A data-driven biocomputing pipeline with meta-analysis on high throughput transcriptomics to identify genome-wide miRNA markers associated with type 2 diabetes

Kushan De Silva / Ryan T. Demmer / Daniel Jönsson / Aya Mousa / Andrew Forbes / Joanne Enticott

Heliyon, Vol 8, Iss 2, Pp e08886- (2022)

2022

Abstract: ... expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three ... were conducted on miEAA 2.0. Results: A total of 1256 DE-miRNAs (821 downregulated; 435 upregulated ...

Abstract	Background: MicroRNAs (miRNAs) are sought-after biomarkers of complex, polygenic diseases such as type 2 diabetes (T2D). Data-driven biocomputing provides robust and novel avenues for synthesizing evidence from individual miRNA seq studies. Objective: To identify miRNA markers associated with T2D, via a data-driven, biocomputing approach on high throughput transcriptomics. Materials and methods: The pipeline consisted of five sequential steps using miRNA seq data retrieved from the National Center for Biotechnology Information Gene Expression Omnibus platform: systematic review; identification of differentially expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three normalization algorithms (trimmed mean of M-values; upper quartile; relative log expression) and two meta-analytic algorithms (robust rank aggregation; Fisher's method of p-value combining) were integrated into the pipeline. Network analysis was conducted on miRNet 2.0 while enrichment and over-representation analyses were conducted on miEAA 2.0. Results: A total of 1256 DE-miRNAs (821 downregulated; 435 upregulated) were identified from 5 eligible miRNA seq datasets (3 circulatory; 1 adipose; 1 pancreatic). The meta-signature comprised 9 miRNAs (hsa-miR-15b-5p; hsa-miR-33b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p; hsa-miR-483-5p; hsa-miR-539-3p; hsa-miR-1260a; hsa-miR-4454), identified via the two meta-analysis approaches. Two hub nodes (hsa-miR-106b-5p; hsa-miR-15b-5p) with above-average degree and betweenness centralities in the miRNA-gene interactions network were identified. Downstream analyses revealed 5 highly conserved- (hsa-miR-33b-5p; hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p) and 7 highly confident- (hsa-miR-33b-5p; hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p; hsa-miR-483-5p; hsa-miR-539-3p) miRNAs. A total of 288 miRNA-disease associations were identified, in which 3 miRNAs (hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-146a-5p) were ...
Keywords	Biomarkers ; Differential expression ; High throughput transcriptomics ; Meta-analysis ; Micro-RNAs ; Type 2 diabetes ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	500
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article: Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies.

De Silva, Kushan / Enticott, Joanne / Barton, Christopher / Forbes, Andrew / Saha, Sajal / Nikam, Rujuta

Digital health

2021 Volume 7, Page(s) 20552076211047390

Abstract: Objective: Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these ... ...

Abstract	Objective: Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance. Methods: The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly-Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes. Ethics and dissemination: No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals. Prospero registration number: CRD42019130886.
Language	English
Publishing date	2021-09-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819396-9
ISSN	2055-2076
ISSN	2055-2076
DOI	10.1177/20552076211047390
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Article ; Online: A combined strategy of feature selection and machine learning to identify predictors of prediabetes.

De Silva, Kushan / Jönsson, Daniel / Demmer, Ryan T

Journal of the American Medical Informatics Association : JAMIA

2019 Volume 27, Issue 3, Page(s) 396–406

Abstract: Objective: To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.: Materials and methods: We analyzed n = 6346 men and women enrolled in the National Health and ... ...

Abstract	Objective: To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population. Materials and methods: We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance. Results: Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05). Discussion: Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified. Conclusion: This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.
MeSH term(s)	Adult ; Algorithms ; Area Under Curve ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Models, Theoretical ; Nutrition Surveys ; Prediabetic State/diagnosis ; Prediabetic State/epidemiology ; Prevalence ; ROC Curve ; Risk Factors ; Socioeconomic Factors
Language	English
Publishing date	2019-12-31
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocz204
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Article ; Online: Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

De Silva, Kushan / Demmer, Ryan T. / Jönsson, Daniel / Mousa, Aya / Forbes, Andrew D. W. / Enticott, Joanne

Funct Integr Genomics. 2022 Oct., v. 22, no. 5 p.1003-1029

2022

Abstract	Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.
Keywords	adults ; apoptosis ; bioinformatics ; etiology ; gene expression ; gene expression regulation ; genes ; genomics ; insulin ; meta-analysis ; metabolomics ; microarray technology ; models ; noninsulin-dependent diabetes mellitus ; pathogenesis ; systematic review
Language	English
Dates of publication	2022-10
Size	p. 1003-1029.
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
ZDB-ID	2014670-X
ISSN	1438-7948 ; 1438-793X
ISSN (online)	1438-7948
ISSN	1438-793X
DOI	10.1007/s10142-022-00881-5
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Article ; Online: A data-driven biocomputing pipeline with meta-analysis on high throughput transcriptomics to identify genome-wide miRNA markers associated with type 2 diabetes

De Silva, Kushan / Demmer, Ryan T. / Jönsson, Daniel / Mousa, Aya / Forbes, Andrew D. W. / Enticott, Joanne

Heliyon. 2022 Feb., v. 8, no. 2 p.e08886-

2022

Abstract: ... platform: systematic review; identification of differentially expressed miRNAs (DE-miRNAs); meta-analysis ... of DE-miRNAs; network analysis; and downstream analyses. Three normalization algorithms (trimmed mean ... A total of 1256 DE-miRNAs (821 downregulated; 435 upregulated) were identified from 5 eligible miRNA seq ...

Abstract	MicroRNAs (miRNAs) are sought-after biomarkers of complex, polygenic diseases such as type 2 diabetes (T2D). Data-driven biocomputing provides robust and novel avenues for synthesizing evidence from individual miRNA seq studies. To identify miRNA markers associated with T2D, via a data-driven, biocomputing approach on high throughput transcriptomics. The pipeline consisted of five sequential steps using miRNA seq data retrieved from the National Center for Biotechnology Information Gene Expression Omnibus platform: systematic review; identification of differentially expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three normalization algorithms (trimmed mean of M-values; upper quartile; relative log expression) and two meta-analytic algorithms (robust rank aggregation; Fisher's method of p-value combining) were integrated into the pipeline. Network analysis was conducted on miRNet 2.0 while enrichment and over-representation analyses were conducted on miEAA 2.0. A total of 1256 DE-miRNAs (821 downregulated; 435 upregulated) were identified from 5 eligible miRNA seq datasets (3 circulatory; 1 adipose; 1 pancreatic). The meta-signature comprised 9 miRNAs (hsa-miR-15b-5p; hsa-miR-33b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p; hsa-miR-483-5p; hsa-miR-539-3p; hsa-miR-1260a; hsa-miR-4454), identified via the two meta-analysis approaches. Two hub nodes (hsa-miR-106b-5p; hsa-miR-15b-5p) with above-average degree and betweenness centralities in the miRNA-gene interactions network were identified. Downstream analyses revealed 5 highly conserved- (hsa-miR-33b-5p; hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p) and 7 highly confident- (hsa-miR-33b-5p; hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p; hsa-miR-483-5p; hsa-miR-539-3p) miRNAs. A total of 288 miRNA-disease associations were identified, in which 3 miRNAs (hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-146a-5p) were highly enriched. A meta-signature of DE-miRNAs associated with T2D was discovered via in-silico analyses and its pathobiological relevance was validated against corroboratory evidence from contemporary studies and downstream analyses. The miRNA meta-signature could be useful for guiding future studies on T2D. There may also be avenues for using the pipeline more broadly for evidence synthesis on other conditions using high throughput transcriptomics.
Keywords	National Center for Biotechnology Information ; biomarkers ; computer simulation ; data collection ; gene expression ; meta-analysis ; microRNA ; noninsulin-dependent diabetes mellitus ; systematic review ; transcriptomics ; Differential expression ; High throughput transcriptomics ; Micro-RNAs ; Type 2 diabetes
Language	English
Dates of publication	2022-02
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e08886
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings

Kushan De Silva / Joanne Enticott / Christopher Barton / Andrew Forbes / Sajal Saha / Rujuta Nikam

Digital Health, Vol

Protocol for a systematic review and meta-analysis of predictive modeling studies

2021 Volume 7

Abstract: Objective Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these ... ...

Abstract	Objective Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance. Methods The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly–Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes. Ethics and dissemination No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals. PROSPERO registration number CRD42019130886
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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