AIMC Topic: Intensive Care Units

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Predicting vital sign deterioration with artificial intelligence or machine learning.

Journal of clinical monitoring and computing Jun 28, 2019
Humans Artificial Intelligence Machine Learning Intensive Care Units Tachycardia
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Artificial intelligence in intensive care: are we there yet?

Intensive care medicine Jun 24, 2019
Artificial Intelligence Machine Learning Humans Intensive Care Units Critical Care
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Physicochemical stability of nefopam and nefopam/droperidol solutions in polypropylene syringes for intensive care units.

European journal of hospital pharmacy : science and practice Jun 11, 2019
INTRODUCTION: Nefopam has been reported to be effective in postoperative pain control with an opioid-sparing effect, but the use of nefopam can lead to nausea and vomiting. To prevent these side effects, droperidol can be mixed with nefopam. In inten...
Nefopam Chemical Phenomena Pharmaceutical Solutions Humans Polypropylenes Syringes Intensive Care Units Droperidol Chromatography, High Pressure Liquid
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Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.

Scientific reports May 29, 2019
Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial i...
Humans Prospective Studies Critical Illness Intensive Care Units Wearable Electronic Devices Male Critical Care Female Remote Sensing Technology Middle Aged Video Recording Feasibility Studies Aged Deep Learning Accelerometry Delirium Adult Pilot Projects
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An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes.

Computer methods and programs in biomedicine May 25, 2019
BACKGROUND AND OBJECTIVE: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective,...
Electronic Health Records Reproducibility of Results Deep Learning Models, Statistical Neural Networks, Computer Algorithms Intensive Care Units Regression Analysis Critical Care Medical Informatics International Classification of Diseases Humans
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Is artificial intelligence (AI) at the doorstep of Intensive Care Units (ICU) and operating room (OR)?

Anaesthesia, critical care & pain medicine May 15, 2019
Intraoperative Care Operating Rooms Artificial Intelligence Intensive Care Units Critical Care Humans
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A machine learning approach for predicting urine output after fluid administration.

Computer methods and programs in biomedicine May 13, 2019
BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation.
Kidney Function Tests Area Under Curve Male Fluid Therapy Humans Middle Aged Critical Care Machine Learning Sepsis Urination Algorithms Decision Support Systems, Clinical Resuscitation Intensive Care Units Risk Factors Electronic Health Records Aged Female Sensitivity and Specificity
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Analysis of parameters affecting blood oxygen saturation and modeling of fuzzy logic system for inspired oxygen prediction.

Computer methods and programs in biomedicine Apr 30, 2019
BACKGROUND AND OBJECTIVE: Fraction of Inspired Oxygen is one of the arbitrary set ventilator parameters which has critical influence on the concentration of blood oxygen. Normally mechanical ventilators providing respiratory assistance are tuned manu...
Decision Making Oxygen Critical Care Therapy, Computer-Assisted Respiration, Artificial Decision Support Systems, Clinical Fuzzy Logic Humans Machine Learning Pulmonary Gas Exchange Software Oximetry Intensive Care Units Reproducibility of Results
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Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Computers in biology and medicine Apr 24, 2019
OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for...
Adolescent Vital Signs Middle Aged Severity of Illness Index Diagnosis, Computer-Assisted Sepsis Aged Adult Male Databases, Factual Intensive Care Units Electronic Health Records Female Humans Machine Learning
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Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care.

Critical care (London, England) Apr 8, 2019
BACKGROUND AND OBJECTIVES: Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volum...
Critical Care Machine Learning Middle Aged Oliguria Humans Acute Kidney Injury Time Factors Aged Intensive Care Units Logistic Models Aged, 80 and over Female Fluid Therapy Male
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