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Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Intensive care medicine, 2020-03, Vol.46 (3), p.383-400

2020

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Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example

Critical care medicine, 2021-06, Vol.49 (6), p.e563-e577

2021

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Machine learning in intensive care medicine: ready for take-off?

Intensive care medicine, 2020-07, Vol.46 (7), p.1486-1488

2020

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Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model

Critical care medicine, 2023-02, Vol.51 (2), p.291-300

2023

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Application of the Sepsis-3 criteria to describe sepsis epidemiology in the Amsterdam UMCdb intensive care dataset

PloS one, 2024-06, Vol.19 (6), p.e0304133

2024

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Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists

Critical care medicine, 2022-06, Vol.50 (6), p.e581-e588

2022

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Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

The journals of gerontology. Series A, Biological sciences and medical sciences, 2023-03, Vol.78 (4), p.718-726

2023

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Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment

Critical care medicine, 2024-02, Vol.52 (2), p.e79-e88

2024

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External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients

Antimicrobial agents and chemotherapy, 2019-05, Vol.63 (5)

2019

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Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial

Critical care (London, England), 2022-09, Vol.26 (1), p.1-265, Article 265

2022

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The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Value in health, 2022-03, Vol.25 (3), p.359-367

2022

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Behavioural artificial intelligence technology for COVID-19 intensivist triage decisions: making the implicit explicit

Intensive care medicine, 2021-11, Vol.47 (11), p.1327-1328

2021

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Amino Acid Loss during Continuous Venovenous Hemofiltration in Critically Ill Patients

Blood purification, 2019-12, Vol.48 (4), p.321-329

2019

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Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts

Critical care (London, England), 2023-02, Vol.27 (1), p.67-67, Article 67

2023

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Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi

International journal of medical informatics (Shannon, Ireland), 2023-10, Vol.178, p.105200-105200, Article 105200

2023

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A novel Vascular Leak Index identifies sepsis patients with a higher risk for in-hospital death and fluid accumulation

Critical care (London, England), 2022-04, Vol.26 (1), p.103-10, Article 103

2022

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Augmented intelligence facilitates concept mapping across different electronic health records

International journal of medical informatics (Shannon, Ireland), 2023-11, Vol.179, p.105233-105233, Article 105233

2023

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Encouraging responsible intensive care data sharing

Intensive care medicine, 2023-08, Vol.49 (8), p.1027-1028

2023

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Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis

Journal of intensive care medicine, 2023-07, Vol.38 (7), p.612-629

2023

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Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation

The Lancet. Digital health, 2023-10, Vol.5 (10), p.e657-e667

2023

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Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study

JMIR human factors, 2023-01, Vol.10, p.e39114-e39114

2023

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Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Critical care explorations, 2021-09, Vol.3 (9), p.e0529-e0529

2021

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Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation

GigaByte (Hong Kong, China), 2022-03, Vol.2022, p.gigabyte45-7

2022

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Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients

Frontiers in pharmacology, 2020-05, Vol.11, p.646

2020

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Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock - rationale and design of a multicenter randomized controlled superiority trial

Trials, 2019-12, Vol.20 (1), p.745-13, Article 745

2019

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Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example

Machine learning in intensive care medicine: ready for take-off?

Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model

Application of the Sepsis-3 criteria to describe sepsis epidemiology in the Amsterdam UMCdb intensive care dataset

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment

External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients

Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Behavioural artificial intelligence technology for COVID-19 intensivist triage decisions: making the implicit explicit

Amino Acid Loss during Continuous Venovenous Hemofiltration in Critically Ill Patients

Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts

Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi

A novel Vascular Leak Index identifies sepsis patients with a higher risk for in-hospital death and fluid accumulation

Augmented intelligence facilitates concept mapping across different electronic health records

Encouraging responsible intensive care data sharing

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis

Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation

Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation

Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients

Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock - rationale and design of a multicenter randomized controlled superiority trial

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