Detalhe da pesquisa
1.
COVID-19 Critical Illness: A Data-Driven Review.
Annu Rev Med
; 73: 95-111, 2022 01 27.
Artigo
em Inglês
| MEDLINE | ID: mdl-34520220
2.
A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.
Crit Care Med
; 49(8): 1312-1321, 2021 08 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-33711001
3.
Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.
Crit Care Med
; 47(11): 1477-1484, 2019 11.
Artigo
em Inglês
| MEDLINE | ID: mdl-31135500
4.
A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.
Crit Care Med
; 47(11): 1485-1492, 2019 11.
Artigo
em Inglês
| MEDLINE | ID: mdl-31389839
5.
Hospital-Onset Sepsis Warrants Expanded Investigation and Consideration as a Unique Clinical Entity.
Chest
; 2024 Jan 19.
Artigo
em Inglês
| MEDLINE | ID: mdl-38246522
6.
Association of Time of Day with Delays in Antimicrobial Initiation among Ward Patients with Hospital-Onset Sepsis.
Ann Am Thorac Soc
; 20(9): 1299-1308, 2023 09.
Artigo
em Inglês
| MEDLINE | ID: mdl-37166187
7.
Association of Unit Census with Delays in Antimicrobial Initiation among Ward Patients with Hospital-acquired Sepsis.
Ann Am Thorac Soc
; 19(9): 1525-1533, 2022 09.
Artigo
em Inglês
| MEDLINE | ID: mdl-35312462
8.
Association of Time to Rapid Response Team Activation With Patient Outcomes Using a Range of Physiologic Deterioration Thresholds.
Crit Care Explor
; 4(11): e0786, 2022 Nov.
Artigo
em Inglês
| MEDLINE | ID: mdl-36349290
9.
Impact of COVID-19 on inpatient clinical emergencies: A single-center experience.
Resusc Plus
; 6: 100135, 2021 Jun.
Artigo
em Inglês
| MEDLINE | ID: mdl-33969324