- Home
- Publications
- Publication Search
- Publication Details
Title
Modern Learning from Big Data in Critical Care: Primum Non Nocere
Authors
Keywords
-
Journal
Neurocritical Care
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-05-06
DOI
10.1007/s12028-022-01510-6
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Missing data in prediction research: A five step approach for multiple imputation, illustrated in the CENTER-TBI study
- (2021) Benjamin Gravesteijn et al. JOURNAL OF NEUROTRAUMA
- Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration
- (2021) Patrick J. Thoral et al. CRITICAL CARE MEDICINE
- Association of clinical sub-phenotypes and clinical deterioration in COVID-19: further cluster analyses
- (2021) Michiel Schinkel et al. INTENSIVE CARE MEDICINE
- Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse
- (2021) Lucas M. Fleuren et al. INTENSIVE CARE MEDICINE
- Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the “large N, small p” setting
- (2021) Peter C Austin et al. STATISTICAL METHODS IN MEDICAL RESEARCH
- Minimum sample size for external validation of a clinical prediction model with a binary outcome
- (2021) Richard D. Riley et al. STATISTICS IN MEDICINE
- External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients
- (2021) Andrew Wong et al. JAMA Internal Medicine
- Joint Modeling of Longitudinal Markers and Time-to-Event Outcomes: An Application and Tutorial in Patients After Surgical Repair of Transposition of the Great Arteries
- (2021) Sara J. Baart et al. Circulation-Cardiovascular Quality and Outcomes
- Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination
- (2021) Esmee Venema et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- Hierarchical Cluster Analysis Identifies Distinct Physiological States After Acute Brain Injury
- (2021) Swarna Rajagopalan et al. Neurocritical Care
- Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review
- (2021) Wei Zhang et al. PLoS One
- Unsupervised machine learning reveals novel traumatic brain injury patient phenotypes with distinct acute injury profiles and long-term outcomes
- (2020) Kaitlin Folweiler et al. JOURNAL OF NEUROTRAUMA
- Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network
- (2020) Qiong Wang et al. PLoS One
- Early prediction of circulatory failure in the intensive care unit using machine learning
- (2020) Stephanie L. Hyland et al. NATURE MEDICINE
- Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study
- (2020) Ben Van Calster et al. STATISTICAL METHODS IN MEDICAL RESEARCH
- Blood biomarkers on admission in acute traumatic brain injury: Relations to severity, CT findings and care path in the CENTER-TBI study
- (2020) Endre Czeiter et al. EBioMedicine
- Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis
- (2020) INTENSIVE CARE MEDICINE
- Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies
- (2020) Maarten van Smeden et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
- (2019) Robert F. Wolff et al. ANNALS OF INTERNAL MEDICINE
- A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
- (2019) Evangelia Christodoulou et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis
- (2019) Joost D. J. Plate et al. BMC Medical Research Methodology
- Towards a new multidimensional classification of traumatic brain injury: a CENTER-TBI study
- (2019) Benjamin Gravesteijn et al. JOURNAL OF NEUROTRAUMA
- Repeated Measures Designs and Analysis of Longitudinal Data
- (2018) Patrick Schober et al. ANESTHESIA AND ANALGESIA
- Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes
- (2018) Bing Si et al. PLoS One
- Sample size for binary logistic prediction models: Beyond events per variable criteria
- (2018) Maarten van Smeden et al. STATISTICAL METHODS IN MEDICAL RESEARCH
- Automated deep-neural-network surveillance of cranial images for acute neurologic events
- (2018) Joseph J. Titano et al. NATURE MEDICINE
- External validation of computed tomography decision rules for minor head injury: prospective, multicentre cohort study in the Netherlands
- (2018) Kelly A Foks et al. BMJ-British Medical Journal
- Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
- (2018) Sasank Chilamkurthy et al. LANCET
- An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets
- (2018) Hyunkwang Lee et al. Nature Biomedical Engineering
- Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
- (2017) Konstantinos Kamnitsas et al. MEDICAL IMAGE ANALYSIS
- Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials
- (2016) David M. Kent et al. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
- Prediction models need appropriate internal, internal–external, and external validation
- (2016) Ewout W. Steyerberg et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- A calibration hierarchy for risk models was defined: from utopia to empirical data
- (2016) Ben Van Calster et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- MIMIC-III, a freely accessible critical care database
- (2016) Alistair E.W. Johnson et al. Scientific Data
- Consensus statement from the 2014 International Microdialysis Forum
- (2015) Peter J. Hutchinson et al. INTENSIVE CARE MEDICINE
- Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI)
- (2015) Andrew I.R. Maas et al. NEUROSURGERY
- Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
- (2015) Tjeerd van der Ploeg et al. BMC Medical Research Methodology
- Body Mass Index Categories and Mortality Risk in US Adults: The Effect of Overweight and Obesity on Advancing Death
- (2014) Luisa N. Borrell et al. AMERICAN JOURNAL OF PUBLIC HEALTH
- External validation of multivariable prediction models: a systematic review of methodological conduct and reporting
- (2014) Gary S Collins et al. BMC Medical Research Methodology
- Towards better clinical prediction models: seven steps for development and an ABCD for validation
- (2014) E. W. Steyerberg et al. EUROPEAN HEART JOURNAL
- How to Use a Subgroup Analysis
- (2014) Xin Sun et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Validation study in four health-care databases: upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk
- (2014) Vera E. Valkhoff et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- The Glasgow Coma Scale at 40 years: standing the test of time
- (2014) Graham Teasdale et al. LANCET NEUROLOGY
- STRengthening Analytical Thinking for Observational Studies: the STRATOS initiative
- (2014) Willi Sauerbrei et al. STATISTICS IN MEDICINE
- Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
- (2013) Ewout W. Steyerberg et al. PLOS MEDICINE
- Risk prediction models: II. External validation, model updating, and impact assessment
- (2012) Karel G M Moons et al. HEART
- Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms
- (2011) O. Naggara et al. AMERICAN JOURNAL OF NEURORADIOLOGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started