scholarly journals A Systematic Review of Machine Learning Models for Predicting Outcomes of Stroke with Structured Data

2020 ◽  
Author(s):  
Wenjuan Wang ◽  
Martin Kiik ◽  
Niels Peek ◽  
Vasa Curcin ◽  
Iain J. Marshall ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Structured Data ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A systematic review of machine learning models for predicting outcomes of stroke with structured data

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234722
Author(s):  
Wenjuan Wang ◽  
Martin Kiik ◽  
Niels Peek ◽  
Vasa Curcin ◽  
Iain J. Marshall ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Structured Data ◽  
Learning Models ◽  
Machine Learning Models

Systematic Review of Machine Learning Models for Personalised Dosing of Heparin

2021 ◽  
Author(s):  
Nazanin Falconer ◽  
Ahmad Abdel‐Hafez ◽  
Ian A. Scott ◽  
Sven Marxen ◽  
Stephen Canaris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Models ◽  
Machine Learning Models

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

scholarly journals Evaluating Machine Learning Models for Sepsis Prediction: A Systematic Review of Methodologies

iScience ◽  
2021 ◽  
pp. 103651
Author(s):  
Hong-Fei Deng ◽  
Ming-Wei Sun ◽  
Yu Wang ◽  
Jun Zeng ◽  
Ting Yuan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Models ◽  
Machine Learning Models

Prediction of the prognosis of oesophageal cancer: Systematic review & meta-analysis of machine learning solutions- Study protocol

2021 ◽  
Author(s):  
Ali Haider Bangash
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cancer Patients ◽  
Oesophageal Cancer ◽  
Study Protocol ◽  
Meta Analysis ◽  
Learning Models ◽  
Collaborative Project ◽  
International Collaborative ◽  
Machine Learning Models

In an international collaborative project, we shall be exploring the features of machine learning models that predict the outcome & prognosis of oesophageal cancer patients.

scholarly journals 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

2021 ◽  
Vol 7 ◽  
pp. 205520762110473
Author(s):  
Kushan De Silva ◽  
Joanne Enticott ◽  
Christopher Barton ◽  
Andrew Forbes ◽  
Sajal Saha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Type 2 Diabetes ◽  
Predictive Modeling ◽  
Prediction Models ◽  
Community Care ◽  
Meta Analysis ◽  
Learning Models ◽  
Machine Learning Models

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

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