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Artificial Intelligence
BMC Med Inform Decis Mak. 2021 May 13;21(1):156. doi: 10.1186/s12911-021-01517-7. ABSTRACT BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood...
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AMIA Annu Symp Proc. 2021 Jan 25;2020:629-637. eCollection 2020. ABSTRACT Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated the performance of the deep learning models...
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Int J Med Sci. 2021 Feb 18;18(8):1739-1745. doi: 10.7150/ijms.51235. eCollection 2021. ABSTRACT Objective: This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission versus intensive care unit (ICU) admission and to predict mortality in COVID-19 pandemic. Materials and methods: This retrospective study consisted...
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PLoS One. 2020 Dec 17;15(12):e0242878. doi: 10.1371/journal.pone.0242878. eCollection 2020. ABSTRACT 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 through...
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Sensors (Basel). 2021 Feb 21;21(4):1495. doi: 10.3390/s21041495. ABSTRACT 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 study,...
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Br J Anaesth. 2021 Feb;126(2):404-414. doi: 10.1016/j.bja.2020.09.044. Epub 2020 Nov 17. ABSTRACT 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. Daily...
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Lancet Digit Health. 2020 Apr;2(4):e179-e191. doi: 10.1016/S2589-7500(20)30018-2. Epub 2020 Mar 12. ABSTRACT 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 ICU...
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J Med Internet Res. 2020 Oct 28;22(10):e21801. doi: 10.2196/21801. ABSTRACT 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 explore...
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Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5442-5445. doi: 10.1109/EMBC44109.2020.9175889. ABSTRACT 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 length...
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BMC Med Inform Decis Mak. 2020 Oct 8;20(1):257. doi: 10.1186/s12911-020-01276-x. ABSTRACT 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 needs...
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