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1
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
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A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Journal of clinical epidemiology, 2019-06, Vol.110, p.12-22 [Peer Reviewed Journal]

2019 Elsevier Inc. ;Copyright © 2019 Elsevier Inc. All rights reserved. ;2019. Elsevier Inc. ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2019.02.004 ;PMID: 30763612

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2
Correction: External validation of risk prediction models for incident colorectal cancer using UK Biobank
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Correction: External validation of risk prediction models for incident colorectal cancer using UK Biobank

British journal of cancer, 2020-05, Vol.122 (10), p.1572-1575 [Peer Reviewed Journal]

The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 0007-0920 ;EISSN: 1532-1827 ;DOI: 10.1038/s41416-020-0767-0 ;PMID: 32203217

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3
Calibration: the Achilles heel of predictive analytics
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Calibration: the Achilles heel of predictive analytics

BMC medicine, 2019-12, Vol.17 (1), p.230-230, Article 230 [Peer Reviewed Journal]

COPYRIGHT 2019 BioMed Central Ltd. ;COPYRIGHT 2019 BioMed Central Ltd. ;2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2019 ;ISSN: 1741-7015 ;EISSN: 1741-7015 ;DOI: 10.1186/s12916-019-1466-7 ;PMID: 31842878

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4
Development and reporting of prediction models: guidance for authors from editors of respiratory, sleep, and critical care journals
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Development and reporting of prediction models: guidance for authors from editors of respiratory, sleep, and critical care journals

Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), http://creativecommons.org/licenses/by-nc-nd/4.0/, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess ;ISSN: 0090-3493 ;EISSN: 1530-0293 ;DOI: 10.1097/CCM.0000000000004246

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5
TCT-706 Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR?
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TCT-706 Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR?

Journal of the American College of Cardiology, 2019-10, Vol.74 (13), p.B693-B693 [Peer Reviewed Journal]

2019 ;Copyright Elsevier Limited Oct 1, 2019 ;ISSN: 0735-1097 ;EISSN: 1558-3597 ;DOI: 10.1016/j.jacc.2019.08.836

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6
Prediction Model of Elastic Load Resource Regulation Potential under Hierarchical and Partitioned Dynamic Control Architecture
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Prediction Model of Elastic Load Resource Regulation Potential under Hierarchical and Partitioned Dynamic Control Architecture

Journal of physics. Conference series, 2022-08, Vol.2331 (1), p.12014 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2331/1/012014

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7
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

IEEE access, 2019, Vol.7, p.81542-81554 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2923707 ;CODEN: IAECCG

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8
Author Correction: Estimate the hidden deployment cost of predictive models to improve patient care
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Author Correction: Estimate the hidden deployment cost of predictive models to improve patient care

Nature medicine, 2020-05, Vol.26 (5), p.803-803 [Peer Reviewed Journal]

COPYRIGHT 2020 Nature Publishing Group ;COPYRIGHT 2020 Nature Publishing Group ;Springer Nature America, Inc. 2020. ;ISSN: 1078-8956 ;EISSN: 1546-170X ;DOI: 10.1038/s41591-020-0862-z ;PMID: 32291415

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9
Author Correction: Development and validation of a risk prediction model for work disability: multicohort study
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Author Correction: Development and validation of a risk prediction model for work disability: multicohort study

Scientific reports, 2018-11, Vol.8 (1), p.17224-1, Article 17224 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-35363-x ;PMID: 30442992

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10
Retraction: Time Series Decomposition using Automatic Learning Techniques for Predictive Models (Journal of Physics: Conference Series 1432 012096)
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Retraction: Time Series Decomposition using Automatic Learning Techniques for Predictive Models (Journal of Physics: Conference Series 1432 012096)

Journal of physics. Conference series, 2020-09, Vol.1432 (1), p.12111 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1432/1/012111

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11
Machine-learning predictions of polymer properties with Polymer Genome
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Machine-learning predictions of polymer properties with Polymer Genome

Journal of applied physics, 2020-11, Vol.128 (17) [Peer Reviewed Journal]

Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-8979 ;EISSN: 1089-7550 ;DOI: 10.1063/5.0023759 ;CODEN: JAPIAU

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12
A data plane security model of segmented routing based on SDP trust enhancement architecture
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A data plane security model of segmented routing based on SDP trust enhancement architecture

Scientific reports, 2022-05, Vol.12 (1), p.8762-8762, Article 8762 [Peer Reviewed Journal]

2022. The Author(s). ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-022-12858-2 ;PMID: 35610296

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13
A guide to machine learning for biologists
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Article
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A guide to machine learning for biologists

Nature reviews. Molecular cell biology, 2022-01, Vol.23 (1), p.40-55 [Peer Reviewed Journal]

2021. Springer Nature Limited. ;Springer Nature Limited 2021. ;ISSN: 1471-0072 ;EISSN: 1471-0080 ;DOI: 10.1038/s41580-021-00407-0 ;PMID: 34518686

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14
Calculating the sample size required for developing a clinical prediction model
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Calculating the sample size required for developing a clinical prediction model

BMJ (Online), 2020-03, Vol.368, p.m441-m441 [Peer Reviewed Journal]

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to ;Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go tohttp://group.bmj.com/group/rights-licensing/permissions2020BMJ ;ISSN: 1756-1833 ;EISSN: 1756-1833 ;DOI: 10.1136/bmj.m441 ;PMID: 32188600

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15
Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small
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Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small

Journal of clinical epidemiology, 2021-04, Vol.132, p.88-96 [Peer Reviewed Journal]

2021 The Authors ;Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved. ;2021. The Authors ;2021 The Authors 2021 ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2020.12.005 ;PMID: 33307188

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16
TCTAP A-001 A Novel Atrial Fibrillation Prediction Model for Asian Subjects - A Nationwide Cohort Study
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TCTAP A-001 A Novel Atrial Fibrillation Prediction Model for Asian Subjects - A Nationwide Cohort Study

Journal of the American College of Cardiology, 2019-04, Vol.73 (15), p.S1-S1 [Peer Reviewed Journal]

2019 ;Copyright Elsevier Limited Apr 23, 2019 ;ISSN: 0735-1097 ;EISSN: 1558-3597 ;DOI: 10.1016/j.jacc.2019.03.026

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17
External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb
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External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb

Journal of clinical epidemiology, 2021-07, Vol.135, p.79-89 [Peer Reviewed Journal]

2021 The Authors ;Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved. ;2021. The Authors ;2021 The Authors 2021 ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2021.02.011 ;PMID: 33596458

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18
Machine learning small molecule properties in drug discovery
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Article
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Machine learning small molecule properties in drug discovery

Artificial intelligence chemistry, 2023-12, Vol.1 (2), p.100020 [Peer Reviewed Journal]

EISSN: 2949-7477 ;DOI: 10.1016/j.aichem.2023.100020

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19
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies in medical imaging
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Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies in medical imaging

BMJ (Online), 2020-03, Vol.368 [Peer Reviewed Journal]

Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. ;2020 Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. BMJ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 1756-1833 ;DOI: 10.1136/bmj.m689

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20
Missing data should be handled differently for prediction than for description or causal explanation
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Missing data should be handled differently for prediction than for description or causal explanation

Journal of clinical epidemiology, 2020-09, Vol.125, p.183-187 [Peer Reviewed Journal]

2020 Elsevier Inc. ;Copyright © 2020 Elsevier Inc. All rights reserved. ;2020. Elsevier Inc. ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2020.03.028 ;PMID: 32540389

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