About: A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

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scholarly article by Evangelia Christodoulou et al published June 2019 in Journal of Clinical Epidemiology

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rdf:type	Item
description	wetenschappelijk artikel (nl) article scientifique publié en 2019 (fr) im Juni 2019 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована в червні 2019 (uk) 2019 ജൂണ്‍ 1 നു പ്രസിദ്ധീകരിച്ച ശാസ്ത്ര ലേഖനം (ml) scholarly article by Evangelia Christodoulou et al published June 2019 in Journal of Clinical Epidemiology (en)
publication date	wds:Q62673862-2793429F-19ED-4705-99DD-328C70185EE3
publication date	2019-06-01 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q62673862-1B6B5D11-6C70-4A1F-A9E9-4CB854A5B09D
language of work or name	English
author name string	wds:Q62673862-1A0CA201-64CD-48E9-A4D3-1AD46A9E1E03 wds:Q62673862-582BECAF-4219-437F-9F1F-98B39F4C3188 wds:Q62673862-5D789D46-5334-4179-956B-16E1B231F71C wds:Q62673862-891C65CE-9FD8-4087-B0CE-50B005A1D0CC wds:Q62673862-E71DAE89-FCCA-477F-ADF9-A8FA2712CEB0 wds:Q62673862-F373EC13-DEA8-4E82-8559-E18CF65ADF2F wds:Q62673862-FB32632D-FF10-4914-90B5-8E647118823C
author name string	Jie Ma Ben Van Calster Gary S Collins Jan Y Verbakel Gary S. Collins Evangelia Christodoulou Jan Y. Verbakel
rdfs:label	A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (en) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (nl)
skos:prefLabel	A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (en) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (nl)
name	A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (en) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (nl)
author	wds:Q62673862-4342DF88-B61A-45CB-B667-5E6C6CFDD084 wds:Q62673862-882DED83-5521-49B4-82D8-5B58F75BEB06
author	Ewout W. Steyerberg
title	wds:Q62673862-55C4FF0A-A9AB-48B6-883E-457B6F111988
title	A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models (en)
page(s)	wds:Q62673862-AE23693A-A418-4337-AFB8-C209DB368F06
page(s)	12-22
instance of	wds:Q62673862-BD2FD529-19F4-492A-B83B-67485B51B6FB wds:Q62673862-BDB9F1FD-701F-4825-BF78-2804B41AFED2
instance of	meta-analysis scholarly article
main subject	wds:Q62673862-03B25224-572B-4F11-B2A4-97DBB4B7A3AA wds:Q62673862-68A1E781-71FD-4E8A-AF07-952B3837C304 wds:Q62673862-E0D83E24-EB5E-4991-918D-ED248A71D7DC
main subject	machine learning reporting systematic review
PubMed ID	wds:Q62673862-1D5CCAB4-B0C2-43CB-86B3-252F155C6775
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/30763612
PubMed ID	30763612
published in	wds:Q62673862-9A2B619A-CA82-4842-83AB-40EB91CF4BC5
published in	Journal of Clinical Epidemiology
volume	wds:Q62673862-5DBDF235-BC73-4EE6-A247-83ACD65AA548
volume	110
DOI	wds:Q62673862-1E7061DA-C48A-46E1-935E-33BC729A6F66
DOI	http://dx.doi.org/10.1016/J.JCLINEPI.2019.02.004
DOI	10.1016/J.JCLINEPI.2019.02.004
is owl:sameAs of	A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q62673862
is cites work of	Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia Overview of clinical prediction models Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning Machine learning for genetic prediction of psychiatric disorders: a systematic review Intelligent Artificial Intelligence: Present Considerations and Future Implications of Machine Learning Applied to Electrocardiogram Interpretation Going Deep With ECG and Aortic Stenosis: Touchdown or Incomplete Pass? Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains Development and Validation of a Score to Predict Acute Care Use After Initiation of Systemic Therapy for Cancer Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network Reporting quality of studies using machine learning models for medical diagnosis: a systematic review What Is the Best Mix of Population-Wide and High-Risk Targeted Strategies of Primary Stroke and Cardiovascular Disease Prevention? Calibration: the Achilles heel of predictive analytics Artificial Intelligence and Orthopaedics: An Introduction for Clinicians Predictive analytics in health care: how can we know it works? A potential biomarker for treatment stratification in psychosis: evaluation of an [18F] FDOPA PET imaging approach Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction Don’t abandon RCTs in IVF. We don’t even understand them Framework for improving outcome prediction for acute to chronic low back pain transitions Artificial Intelligence and Primary Care Research: A Scoping Review Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes Simple Methods for Evaluating 4 Types of Biomarkers: Surrogate Endpoint, Prognostic, Predictive, and Cancer Screening Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing Developing well-calibrated illness severity scores for decision support in the critically ill Suicide prediction models: a critical review of recent research with recommendations for the way forward Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning Predicting mental health problems in adolescence using machine learning techniques Validation of an oncology-specific opioid risk calculator in cancer survivors Evaluating risk prediction models for adults with heart failure: A systematic literature review Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection Early detection of type 2 diabetes mellitus using machine learning-based prediction models Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality Review of Clinical Research Informatics Praise to Robust Prediction Modeling in Large Datasets Developing Biomarker Panels to Predict Progression of Acute Kidney Injury After Cardiac Surgery Artificial Intelligence Applications in Dermatology: Where Do We Stand? Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives How to use statistical models and methods for clinical prediction Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan

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