A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients

Sensors (Basel). 2021 Feb 21;21(4):1495. doi: 10.3390/s21041495. ABSTRACT ADVERTISEMENT Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this […]

A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients

PLoS One. 2020 Dec 17;15(12):e0242878. doi: 10.1371/journal.pone.0242878. eCollection 2020. ABSTRACT ADVERTISEMENT BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identification of and decision-making for patients showing signs of developing physiological instability during treatment. The aim of this study was to enhance the identification of patients at risk for deterioration […]

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

Lancet Digit Health. 2020 Apr;2(4):e179-e191. doi: 10.1016/S2589-7500(20)30018-2. Epub 2020 Mar 12. ABSTRACT ADVERTISEMENT BACKGROUND: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data improved mortality prognostication for patients in the […]

Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study

Br J Anaesth. 2021 Feb;126(2):404-414. doi: 10.1016/j.bja.2020.09.044. Epub 2020 Nov 17. ABSTRACT ADVERTISEMENT BACKGROUND: We examined whether a context and process-sensitive ‘intelligent’ checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds. METHODS: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. […]

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

J Med Internet Res. 2020 Oct 28;22(10):e21801. doi: 10.2196/21801. ABSTRACT ADVERTISEMENT BACKGROUND: Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to […]

Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5442-5445. doi: 10.1109/EMBC44109.2020.9175889. ABSTRACT ADVERTISEMENT Predicting Cardiovascular Length of stay based hospitalization at the time of patients’ admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently, few studies examined the […]

A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare

BMC Med Inform Decis Mak. 2020 Oct 8;20(1):257. doi: 10.1186/s12911-020-01276-x. ABSTRACT ADVERTISEMENT BACKGROUND: There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, approaches to explaining complex machine learning (ML) models are rarely informed by end-user […]

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

BMC Med Inform Decis Mak. 2020 Oct 2;20(1):251. doi: 10.1186/s12911-020-01271-2. ABSTRACT ADVERTISEMENT BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients […]

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

J Med Internet Res. 2020 Sep 25;22(9):e20268. doi: 10.2196/20268. ABSTRACT ADVERTISEMENT BACKGROUND: Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a different perspective on the performance, they remain to be overall […]

Clinical Predictive Models for COVID-19: Systematic Study

J Med Internet Res. 2020 Oct 6;22(10):e21439. doi: 10.2196/21439. ABSTRACT ADVERTISEMENT BACKGROUND: COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit […]