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1.
Age-related changes in plasma biomarkers and their association with mortality in COVID-19.
Eur Respir J
; 62(1)2023 07.
Artículo
en Inglés
| MEDLINE | ID: mdl-37080568
2.
Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.
J Intensive Care Med
; 38(7): 612-629, 2023 Jul.
Artículo
en Inglés
| MEDLINE | ID: mdl-36744415
3.
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.
Crit Care
; 26(1): 265, 2022 09 05.
Artículo
en Inglés
| MEDLINE | ID: mdl-36064438
4.
Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.
Acta Anaesthesiol Scand
; 66(1): 65-75, 2022 01.
Artículo
en Inglés
| MEDLINE | ID: mdl-34622441
5.
Why we should sample sparsely and aim for a higher target: Lessons from model-based therapeutic drug monitoring of vancomycin in intensive care patients.
Br J Clin Pharmacol
; 87(3): 1234-1242, 2021 03.
Artículo
en Inglés
| MEDLINE | ID: mdl-32715505
6.
Predictors for extubation failure in COVID-19 patients using a machine learning approach.
Crit Care
; 25(1): 448, 2021 12 27.
Artículo
en Inglés
| MEDLINE | ID: mdl-34961537
7.
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.
Crit Care
; 25(1): 304, 2021 08 23.
Artículo
en Inglés
| MEDLINE | ID: mdl-34425864
8.
Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.
Pharm Res
; 37(9): 171, 2020 Aug 23.
Artículo
en Inglés
| MEDLINE | ID: mdl-32830297
9.
External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients.
Antimicrob Agents Chemother
; 63(5)2019 05.
Artículo
en Inglés
| MEDLINE | ID: mdl-30833424
10.
Why physiology will continue to guide the choice between balanced crystalloids and normal saline: a systematic review and meta-analysis.
Crit Care
; 23(1): 366, 2019 11 21.
Artículo
en Inglés
| MEDLINE | ID: mdl-31752973
11.
Clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient: a cross-sectional study.
Crit Care
; 23(1): 185, 2019 05 22.
Artículo
en Inglés
| MEDLINE | ID: mdl-31118061
12.
Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage.
Neurocrit Care
; 28(2): 184-193, 2018 04.
Artículo
en Inglés
| MEDLINE | ID: mdl-28983801
13.
Correction to: Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.
Pharm Res
; 37(11): 223, 2020 Oct 19.
Artículo
en Inglés
| MEDLINE | ID: mdl-37452467
14.
Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi.
Int J Med Inform
; 178: 105200, 2023 10.
Artículo
en Inglés
| MEDLINE | ID: mdl-37703800
15.
Augmented intelligence facilitates concept mapping across different electronic health records.
Int J Med Inform
; 179: 105233, 2023 Nov.
Artículo
en Inglés
| MEDLINE | ID: mdl-37748329
16.
INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.
Shock
; 58(5): 358-365, 2022 11 01.
Artículo
en Inglés
| MEDLINE | ID: mdl-36155964
17.
Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.
Ann Intensive Care
; 12(1): 99, 2022 Oct 20.
Artículo
en Inglés
| MEDLINE | ID: mdl-36264358
18.
Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.
Int J Med Inform
; 167: 104863, 2022 11.
Artículo
en Inglés
| MEDLINE | ID: mdl-36162166
19.
Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis.
Artif Intell Med
; 112: 102003, 2021 02.
Artículo
en Inglés
| MEDLINE | ID: mdl-33581824
20.
[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave]. / Klinisch beloop van covid-19 in Nederland.
Ned Tijdschr Geneeskd
; 1652021 01 11.
Artículo
en Holandés
| MEDLINE | ID: mdl-33651497