This paper is in the following e-collection/theme issue:

Secondary Use of Clinical Data for Research and Surveillance (375)

Published on in Vol 7, No 1 (2019): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13006, first published .
The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

Authors of this article:

Patrick Essay1 Author Orcid Image ;   Tala B Shahin2 Author Orcid Image ;   Baran Balkan1 Author Orcid Image ;   Jarrod Mosier3, 4 Author Orcid Image ;   Vignesh Subbian5, 6 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

Journals

  1. Essay P, Balkan B, Subbian V. Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset. JMIR Medical Informatics 2020;8(8):e19892 View
  2. Deshmukh F, Merchant S. Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit. American Journal of Gastroenterology 2020;115(10):1657 View
  3. Sheikhalishahi S, Balaraman V, Osmani V, Na K. Benchmarking machine learning models on multi-centre eICU critical care dataset. PLOS ONE 2020;15(7):e0235424 View
  4. Essay P, Mosier J, Subbian V. Rule-Based Cohort Definitions for Acute Respiratory Failure: Electronic Phenotyping Algorithm. JMIR Medical Informatics 2020;8(4):e18402 View
  5. O’Halloran H, Kwong K, Veldhoen R, Maslove D. Characterizing the Patients, Hospitals, and Data Quality of the eICU Collaborative Research Database*. Critical Care Medicine 2020;48(12):1737 View
  6. Liu J, Wu J, Liu S, Li M, Hu K, Li K, Adrish M. Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLOS ONE 2021;16(2):e0246306 View
  7. Hayden E, Davis C, Clark S, Joshi A, Krupinski E, Naik N, Ward M, Zachrison K, Olsen E, Chang B, Burner E, Yadav K, Greenwald P, Chandra S. Telehealth in emergency medicine: A consensus conference to map the intersection of telehealth and emergency medicine. Academic Emergency Medicine 2021;28(12):1452 View
  8. Laudanski K, Scruth E, Winterbottom F, Rastrepo M, Dugar S, Herasevich V, Villanueva I, Armaignac D, Scott B. A Survey of Tele-Critical Care State and Needs in 2019 and 2020 Conducted among the Members of the Society of Critical Care Medicine. Healthcare 2022;10(8):1445 View
  9. Franco J, Solad Y, Venkatesh A, Van Tonder R, Solod A, Stachowiak T, Hsiao A, Sangal R. Exploratory Descriptive Analysis of Smart Speaker Utilization in the Emergency Department During the COVID-19 Pandemic. The Journal of Emergency Medicine 2023;64(4):506 View
  10. Gao T, Wang Y. Association between white blood cell count to hemoglobin ratio and risk of in-hospital mortality in patients with lung cancer. BMC Pulmonary Medicine 2023;23(1) View

Books/Policy Documents

  1. Latifi R, Osmani K. Telemedicine, Telehealth and Telepresence. View
  2. Mistry K, Devon K, Ganai S. Difficult Decisions in Surgical Ethics. View
  3. Chouvarda I, Perantoni E, Steiropoulos P. Wearable Sensing and Intelligent Data Analysis for Respiratory Management. View