The myth of generalisability in clinical research and machine learning in health care

scientific article published on 24 August 2020

The myth of generalisability in clinical research and machine learning in health care is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1016/S2589-7500(20)30186-2
P932PMC publication ID7444947
P698PubMed publication ID32864600

P50authorLeo Anthony CeliQ57006426
Finale Doshi-VelezQ64341979
P2093author name stringJoseph Futoma
Trishan Panch
Morgan Simons
P2860cites workEarly warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodologyQ95335001
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Clinical applications of continual learning machine learningQ104485973
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A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictionsQ30746268
Factors that can affect the external validity of randomised controlled trialsQ33251918
Assessing the generalizability of prognostic informationQ33540894
What do we mean by validating a prognostic model?Q33849998
External validity of randomised controlled trials: "to whom do the results of this trial apply?".Q36003704
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External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challengesQ37028590
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary ViewQ37587950
Calibration drift in regression and machine learning models for acute kidney injuryQ38853177
Real-World Evidence - What Is It and What Can It Tell Us?Q40427436
An evaluation of machine-learning methods for predicting pneumonia mortalityQ40906161
Strengths and limitations of early warning scores: A systematic review and narrative synthesis.Q47918969
Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.Q55094449
The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive careQ57804819
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyQ58608483
A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational StudyQ61802679
Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reportingQ64108974
Evidence-based medicine, opinion-based medicine, and real-world medicineQ78398023
The Toughest Triage — Allocating Ventilators in a PandemicQ88507120
Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care JournalsQ90106864
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studiesQ90659911
Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient DeteriorationQ91680324
Continual lifelong learning with neural networks: A reviewQ91705100
Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisalQ91706587
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers-From the Radiology Editorial BoardQ92349502
Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in IndiaQ92716150
P433issue9
P921main subjectmachine learningQ2539
P304page(s)e489-e492
P577publication date2020-08-24
P1433published inThe Lancet Digital HealthQ96321101
P1476titleThe myth of generalisability in clinical research and machine learning in health care
P478volume2

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cites work (P2860)
Q103826077Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches
Q130467315Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

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