Toward a two-tier clinical warning system for hospitalized patients (original) (raw)

Clinical implementation of a machine learning system to detect deteriorating patients reduces time to response and intervention

Santiago Romero Brufau

2021

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Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology

Jean-Marie Aerts

Sensors, 2020

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Automated detection of physiologic deterioration in hospitalized patients

William Tettelbach

2015

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Medical Data Mining for Early Deterioration Warning in General Hospital Wards

Minmin Chen

2011 IEEE 11th International Conference on Data Mining Workshops, 2011

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Thomas Scheeren

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The New American Journal of Medicine, 2021

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Mathieu Bert

Critical care medicine, 2016

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Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring

Andreas Polze

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A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Unit

IRJET Journal

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Multi-parameter vital sign database to assist in alarm optimization for general care units

Scott McCombie

Journal of Clinical Monitoring and Computing, 2015

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Integrated monitoring and analysis for early warning of patient deterioration

Lionel Tarassenko

British Journal of Anaesthesia, 2006

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Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction

Yehudit Aperstein

Journal of Healthcare Engineering, 2019

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A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis

Maximiliano Mollura

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

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Artificial Intelligence, Sensors and Vital Health Signs: A Review

yusuf surakat

Applied Sciences

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Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors

Lionel Tarassenko

IEEE journal of biomedical and health informatics, 2014

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A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

Noha El-Ganainy

IEEE Access

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Optimising Classifiers for the Detection of Physiological Deterioration in Patient Vital-Sign Data

Lionel Tarassenko

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Using Early Warning Score for vital signs analysis in IoT healthcare environment

David Acosta Viana

2019

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MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

Roopa Kohli-Seth

Journal of Clinical Medicine, 2020

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Evaluation of a smart alarm for intensive care using clinical data

Margaret Fortino

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

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Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System

Lionel Tarassenko

American Journal of Respiratory and Critical Care Medicine, 2021

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Intelligent Alarm Processing into Clinical Knowledge

Donald C. Gause

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

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Early hospital mortality prediction using vital signals

William Romine

Smart Health

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A New Paradigm of Technology-Enabled ‘Vital Signs' for Early Detection of Health Change for Older Adults

Richelle Koopman

Gerontology, 2014

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Adaptative vital signs monitoring system based on the early warning score approach in smart hospital context

Abderrazak Jemai

IET Smart Cities, 2021

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ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing

Manuel Santos

Proceedings of the 13th International Conference on Enterprise Information Systems, 2011

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Prediction of ICU Patients’ Deterioration Using Machine Learning Techniques

Yosra Aljubran

Cureus, 2023

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Early recognition of acutely deteriorating patients in non‐intensive care units: Assessment of an innovative monitoring technology

Jeff Sestokas

Journal of Hospital Medicine, 2012

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The Taxonomy: Health Monitoring System Using Machine Learning Techniques

IJIRT Journal

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A Methodology for Evaluating the Performance of Alerting and Detection Algorithms Running on Continuous Patient Data

Larry Eshelman

2017

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