Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning (Q102152351)

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:33208887%20AND%20SRC:MED&resulttype=core&format=json

20 November 2020

title

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning (English)

1 reference

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Intraductal papillary mucinous neoplasm

20 November 2020

main subject

machine learning

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Claudio Bassi

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20 November 2020

author name string

Jae Seung Kang

1

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20 November 2020

Chanhee Lee

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20 November 2020

Wookyeong Song

3

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Wonho Choo

4

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Seungyeoun Lee

5

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Sungyoung Lee

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Youngmin Han

7

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Roberto Salvia

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Giovanni Marchegiani

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Cristopher L Wolfgang

11

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Jin He

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Alex B Blair

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Michael D Kluger

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Gloria H Su

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Ki-Byung Song

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Masakazu Yamamoto

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publication date

18 November 2020

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20 November 2020

volume

10

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20 November 2020

issue

1

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20 November 2020

page(s)

20140

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Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas

20 November 2020

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7 January 2021

Clinical validation of the 2017 international consensus guidelines on intraductal papillary mucinous neoplasm of the pancreas

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7 January 2021

Development and Validation of a Multi-Institutional Preoperative Nomogram for Predicting Grade of Dysplasia in Intraductal Papillary Mucinous Neoplasms (IPMNs) of the Pancreas: A Report from The Pancreatic Surgery Consortium

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7 January 2021

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas

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7 January 2021

New Model for Predicting Malignancy in Patients With Intraductal Papillary Mucinous Neoplasm

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7 January 2021

Machine Learning in Medical Imaging: All Journeys Begin With a Single Step

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Machine Learning Methods for Histopathological Image Analysis

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Machine learning applications in cancer prognosis and prediction

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Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

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7 January 2021

Validation of a nomogram to predict the risk of cancer in patients with intraductal papillary mucinous neoplasm and main duct dilatation of 10 mm or less

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7 January 2021

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

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7 January 2021

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.

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7 January 2021

Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

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7 January 2021

Machine learning, medical diagnosis, and biomedical engineering research - commentary

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Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods

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A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

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7 January 2021

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

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Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches

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7 January 2021

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

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Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

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A Revised Classification System and Recommendations From the Baltimore Consensus Meeting for Neoplastic Precursor Lesions in the Pancreas

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7 January 2021

10.1038/S41598-020-76974-7

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DOI

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20 November 2020

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