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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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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

2023

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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

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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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

2024

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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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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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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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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.103-103, Article 103

2022

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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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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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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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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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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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The ESICM datathon and the ESICM and ICMx data science strategy

Intensive care medicine experimental, 2024-03, Vol.12 (1), p.29-29

2024

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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.745-745, 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

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

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

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

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

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

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

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

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

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

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

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

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

The ESICM datathon and the ESICM and ICMx data science strategy

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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