Related references
Note: Only part of the references are listed.A Survey on Classifying Big Data with Label Noise
Justin M. Johnson et al.
ACM JOURNAL OF DATA AND INFORMATION QUALITY (2022)
MONitoring Knockbacks in EmergencY (MONKEY) - An Audit of Disposition Outcomes in Emergency Patients with Rejected Admission Requests
Wendell Zhang et al.
OPEN ACCESS EMERGENCY MEDICINE (2022)
Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
Michael Moor et al.
FRONTIERS IN MEDICINE (2021)
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Davood Karimi et al.
MEDICAL IMAGE ANALYSIS (2020)
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Samantha Cruz Rivera et al.
NATURE MEDICINE (2020)
Resolving Differences of Opinion between Medical Experts: A Case Study with the IS-DELPHI System
Derek Sleeman et al.
PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 5: HEALTHINF (2020)
A Method to Detect Inconsistent Annotations in a Medical Document using UMLS
Devasish Mahato et al.
PROCEEDINGS OF THE 11TH ANNUAL MEETING OF THE FORUM FOR INFORMATION RETRIEVAL EVALUATION (FIRE 2019) (2019)
Unintended Consequences of Machine Learning in Medicine
Federico Cabitza et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
Measuring inter-rater reliability for nominal data - which coefficients and confidence intervals are appropriate?
Antonia Zapf et al.
BMC MEDICAL RESEARCH METHODOLOGY (2016)
Prediction models need appropriate internal, internal-external, and external validation
Ewout W. Steyerberg et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2016)
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
Wei Luo et al.
JOURNAL OF MEDICAL INTERNET RESEARCH (2016)
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement
G. S. Collins et al.
BRITISH JOURNAL OF SURGERY (2015)
Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings
J. J. Halford et al.
CLINICAL NEUROPHYSIOLOGY (2015)
External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination
George C. M. Siontis et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2015)
Effect of label noise in the complexity of classification problems
Luis P. F. Garcia et al.
NEUROCOMPUTING (2015)
Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance
Carl-Magnus Svensson et al.
JOURNAL OF IMMUNOLOGY RESEARCH (2015)
Towards better clinical prediction models: seven steps for development and an ABCD for validation
Ewout W. Steyerberg et al.
EUROPEAN HEART JOURNAL (2014)
Classification in the Presence of Label Noise: a Survey
Benoit Frenay et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)
DSM-5 Field Trials in the United States and Canada, Part II: Test-Retest Reliability of Selected Categorical Diagnoses
Darrel A. Regier et al.
AMERICAN JOURNAL OF PSYCHIATRY (2013)
Investigating the Disagreement Between Clinicians' Ratings of Patients in ICUs
Simon Rogers et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2013)
Detecting and resolving inconsistencies between domain experts' different perspectives on (classification) tasks
Derek Sleeman et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2012)
Interrater reliability: the kappa statistic
Mary L. McHugh
BIOCHEMIA MEDICA (2012)
Atypical ductal hyperplasia: interobserver and intraobserver variability
Rohit K. Jain et al.
MODERN PATHOLOGY (2011)
Building Watson: An Overview of the DeepQA Project
David Ferrucci et al.
AI MAGAZINE (2010)
A study of the effect of different types of noise on the precision of supervised learning techniques
David F. Nettleton et al.
ARTIFICIAL INTELLIGENCE REVIEW (2010)
Class noise vs. attribute noise: A quantitative study of their impacts
XQ Zhu et al.
ARTIFICIAL INTELLIGENCE REVIEW (2004)
External validation is necessary in, prediction research: A clinical example
SE Bleeker et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2003)
Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach
D Nettleton et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2001)
PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals
AL Goldberger et al.
CIRCULATION (2000)
Share Paper
