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Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

Systematic reviews, 2019-07, Vol.8 (1), p.163-10, Article 163

2019

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RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials

Journal of the American Medical Informatics Association : JAMIA, 2016-01, Vol.23 (1), p.193-201

2016

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Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide

Research synthesis methods, 2018-12, Vol.9 (4), p.602-614

2018

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Semi-automated screening of biomedical citations for systematic reviews

BMC bioinformatics, 2010-01, Vol.11 (1), p.55-55, Article 55

2010

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Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data

BMC medical research methodology, 2009-12, Vol.9 (1), p.80-80, Article 80

2009

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Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

Computational linguistics - Association for Computational Linguistics, 2021-04, Vol.47 (1), p.117-140

2021

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A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews

Journal of the American Medical Informatics Association : JAMIA, 2014-11, Vol.21 (6), p.1098-1103

2014

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Neural information retrieval: at the end of the early years

Information retrieval (Boston), 2018-06, Vol.21 (2-3), p.111-182

2018

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Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study

BMC medical informatics and decision making, 2019-05, Vol.19 (1), p.96-12, Article 96

2019

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Improving class probability estimates for imbalanced data

Knowledge and information systems, 2014-10, Vol.41 (1), p.33-52

2014

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Rapid reviews may produce different results to systematic reviews: a meta-epidemiological study

Journal of clinical epidemiology, 2019-05, Vol.109, p.30-41

2019

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What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature

IJCAI (United States), 2019, Vol.2019, p.6416-6420

Wallace, Byron C

2019

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Computational irony: A survey and new perspectives

The Artificial intelligence review, 2015-04, Vol.43 (4), p.467-483

Wallace, Byron C.

2015

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OpenMEE: Intuitive, open‐source software for meta‐analysis in ecology and evolutionary biology

Methods in ecology and evolution, 2017-08, Vol.8 (8), p.941-947

2017

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State of the evidence: a survey of global disparities in clinical trials

BMJ global health, 2021-01, Vol.6 (1), p.e004145

2021

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Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision

Journal of machine learning research, 2016-01, Vol.17

2016

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Closing the Gap between Methodologists and End-Users: R as a Computational Back-End

Journal of statistical software, 2012-06, Vol.49 (5)

2012

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Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach

Journal of the American Medical Informatics Association : JAMIA, 2017-11, Vol.24 (6), p.1165-1168

2017

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Automatically Annotating Topics in Transcripts of Patient-Provider Interactions via Machine Learning

Medical decision making, 2014-05, Vol.34 (4), p.503-512

2014

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Trialstreamer: A living, automatically updated database of clinical trial reports

Journal of the American Medical Informatics Association : JAMIA, 2020-12, Vol.27 (12), p.1903-1912

2020

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Semi-automated Tools for Systematic Searches

Methods in molecular biology (Clifton, N.J.), 2022, Vol.2345, p.17

2022

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Question answering systems for health professionals at the point of care-a systematic review

Journal of the American Medical Informatics Association : JAMIA, 2024-04, Vol.31 (4), p.1009-1024

2024

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Challenges and Opportunities in Applied Machine Learning

The AI magazine, 2012-03, Vol.33 (1), p.11-24

2012

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In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving

Journal of clinical epidemiology, 2023-01, Vol.153, p.26-33

2023

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Automating Risk of Bias Assessment for Clinical Trials

IEEE journal of biomedical and health informatics, 2015-07, Vol.19 (4), p.1406-1412

2015

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Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials

Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide

Semi-automated screening of biomedical citations for systematic reviews

Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data

Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews

Neural information retrieval: at the end of the early years

Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study

Improving class probability estimates for imbalanced data

Rapid reviews may produce different results to systematic reviews: a meta-epidemiological study

What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature

Computational irony: A survey and new perspectives

OpenMEE: Intuitive, open‐source software for meta‐analysis in ecology and evolutionary biology

State of the evidence: a survey of global disparities in clinical trials

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision

Closing the Gap between Methodologists and End-Users: R as a Computational Back-End

Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach

Automatically Annotating Topics in Transcripts of Patient-Provider Interactions via Machine Learning

Trialstreamer: A living, automatically updated database of clinical trial reports

Semi-automated Tools for Systematic Searches

Question answering systems for health professionals at the point of care-a systematic review

Challenges and Opportunities in Applied Machine Learning

In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving

Automating Risk of Bias Assessment for Clinical Trials

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