相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
Davide Chicco et al.
BIODATA MINING (2021)
Reporting guidelines for artificial intelligence in healthcare research
Hussein Ibrahim et al.
CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY (2021)
External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb
Kym I. E. Snell et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2021)
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Ravi Aggarwal et al.
NPJ DIGITAL MEDICINE (2021)
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael Roberts et al.
NATURE MACHINE INTELLIGENCE (2021)
Clinician checklist for assessing suitability of machine learning applications in healthcare
Ian Scott et al.
BMJ HEALTH & CARE INFORMATICS (2021)
Systematic review and meta-analysis of within-subject and between-subject biological variation estimates of 20 haematological parameters
Abdurrahman Coskun et al.
CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2020)
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Jonathan Waring et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)
Challenges to the Reproducibility of Machine Learning Models in Health Care
Andrew L. Beam et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2020)
The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability
Federico Cabitza et al.
APPLIED SCIENCES-BASEL (2020)
MINIMAR (MINimum Information for Medical Al Reporting): Developing reporting standards for artificial intelligence in health care
Tina Hernandez-Boussard et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)
As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI
Federico Cabitza et al.
BMC MEDICAL INFORMATICS AND DECISION MAKING (2020)
Going on up to the SPIRIT in AI: will new reporting guidelines for clinical trials of AI interventions improve their rigour?
Paul Wicks et al.
BMC MEDICINE (2020)
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group
Viknesh Sounderajah et al.
NATURE MEDICINE (2020)
The data representativeness criterion: Predicting the performance of supervised classification based on data set similarity
Evelien Schat et al.
PLOS ONE (2020)
Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
Joon Lee
JOURNAL OF MEDICAL INTERNET RESEARCH (2020)
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Beau Norgeot et al.
NATURE MEDICINE (2020)
Reporting of artificial intelligence prediction models
Gary S. Collins et al.
LANCET (2019)
Machine Learning in Medicine
Alvin Rajkomar et al.
NEW ENGLAND JOURNAL OF MEDICINE (2019)
Causability and explainability of artificial intelligence in medicine
Andreas Holzinger et al.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2019)
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Evangelia Christodoulou et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2019)
Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review
Indranil Balki et al.
CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES (2019)
Three myths about risk thresholds for prediction models
Laure Wynants et al.
BMC MEDICINE (2019)
Key challenges for delivering clinical impact with artificial intelligence
Christopher J. Kelly et al.
BMC MEDICINE (2019)
Extension of the CONSORT and SPIRIT statements
Xiaoxuan Liu et al.
LANCET (2019)
Machine learning algorithm validation with a limited sample size
Andrius Vabalas et al.
PLOS ONE (2019)
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Robert F. Wolff et al.
ANNALS OF INTERNAL MEDICINE (2019)
Calibration: the Achilles heel of predictive analytics
Ben van Calster et al.
BMC MEDICINE (2019)
Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review
Jiayi Shen et al.
JMIR MEDICAL INFORMATICS (2019)
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Xiaoxuan Liu et al.
LANCET DIGITAL HEALTH (2019)
Predictive analytics in health care: how can we know it works?
Ben Van Calster et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2019)
Does health informatics have a replication crisis?
Enrico Coiera et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2018)
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching et al.
JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)
Artificial intelligence faces reproducibility crisis
Matthew Hutson
SCIENCE (2018)
Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators
Ben Van Calster et al.
EUROPEAN UROLOGY (2018)
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Milena A. Gianfrancesco et al.
JAMA INTERNAL MEDICINE (2018)
Ensuring Fairness in Machine Learning to Advance Health Equity
Alvin Rajkomar et al.
ANNALS OF INTERNAL MEDICINE (2018)
Machine learning in medicine: Addressing ethical challenges
Effy Vayena et al.
PLOS MEDICINE (2018)
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Ke Yan et al.
JOURNAL OF MEDICAL IMAGING (2018)
Artificial intelligence powers digital medicine
Alexander L. Fogel et al.
NPJ DIGITAL MEDICINE (2018)
Unintended Consequences of Machine Learning in Medicine
Federico Cabitza et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
Imbalanced target prediction with pattern discovery on clinical data repositories
Tak-Ming Chan et al.
BMC MEDICAL INFORMATICS AND DECISION MAKING (2017)
Medical big data: promise and challenges
Choong Ho Lee et al.
KIDNEY RESEARCH AND CLINICAL PRACTICE (2017)
Prediction models need appropriate internal, internal-external, and external validation
Ewout W. Steyerberg et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2016)
MIMIC-III, a freely accessible critical care database
Alistair E. W. Johnson et al.
SCIENTIFIC DATA (2016)
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) The TRIPOD Statement
Gary S. Collins et al.
CIRCULATION (2015)
Machine Learning in Medicine
Rahul C. Deo
CIRCULATION (2015)
The precision recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases
Brice Ozenne et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2015)
A classification of chronic pain for ICD-11
Rolf-Detlef Treede et al.
PAIN (2015)
Missing data in medical databases: Impute, delete or classify?
Federico Cismondi et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2013)
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
Carsten F. Dormann et al.
ECOGRAPHY (2013)
Comparison of imputation methods for missing laboratory data in medicine
Akbar K. Waljee et al.
BMJ OPEN (2013)
Toward open sharing of task-based fMRI data: the OpenfMRI project
Russell A. Poldrack et al.
FRONTIERS IN NEUROINFORMATICS (2013)
Leakage in Data Mining: Formulation, Detection, and Avoidance
Shachar Kaufman et al.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2012)
Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods
Simone Borra et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2010)
Randomized Clinical Trials and Observational Studies Guidelines for Assessing Respective Strengths and Limitations
Edward L. Hannan
JACC-CARDIOVASCULAR INTERVENTIONS (2008)
Why most published research findings are false
JPA Ioannidis
PLOS MEDICINE (2005)
The cross-sectional cohort study - An underutilized design
JI Hudson et al.
EPIDEMIOLOGY (2005)
External validation is necessary in, prediction research: A clinical example
SE Bleeker et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2003)
LOINC, a universal standard for identifying laboratory observations: A 5-year update
CJ McDonald et al.
CLINICAL CHEMISTRY (2003)
Summarizing multiple aspects of model performance in a single diagram.
KE Taylor
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES (2001)
分享论文
