AIMC Topic: Critical Care

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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
Intensive Care Units Critical Care Intraoperative Care Operating Rooms Humans Artificial Intelligence
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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 Fluid Therapy Critical Care Male Middle Aged Sepsis Decision Support Systems, Clinical Urination Resuscitation Humans Machine Learning Algorithms Female Intensive Care Units Risk Factors Electronic Health Records Aged 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...
Critical Care Humans Therapy, Computer-Assisted Machine Learning Software Pulmonary Gas Exchange Decision Support Systems, Clinical Oximetry Intensive Care Units Fuzzy Logic Reproducibility of Results Decision Making Respiration, Artificial Oxygen
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Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.

BMC medical informatics and decision making Apr 9, 2019
BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain knowledge should be specified...
Reinforcement, Psychology Clinical Protocols Machine Learning Retrospective Studies Clinical Decision-Making Respiration, Artificial Hypnotics and Sedatives Bayes Theorem Critical Care Markov Chains Humans
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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...
Fluid Therapy Critical Care Oliguria Middle Aged Time Factors Logistic Models Humans Female Aged Machine Learning Aged, 80 and over Male Acute Kidney Injury Intensive Care Units
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Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Journal of clinical monitoring and computing Apr 6, 2019
Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing pat...
Machine Learning Decision Support Techniques Retrospective Studies Intensive Care Units Humans Pattern Recognition, Automated False Positive Reactions Clinical Alarms Monitoring, Physiologic Knowledge Bases Critical Care Expert Systems
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Enabling artificial intelligence in high acuity medical environments.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy Apr 5, 2019
Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as increased efficiency. Yet, only very few applications have been reported because of the limited accessibi...
Humans Critical Care Efficiency, Organizational Surgical Procedures, Operative Artificial Intelligence Decision Support Systems, Clinical Workflow
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Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.

BMJ open Mar 7, 2019
OBJECTIVE: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.
Critical Care Patient Readmission Male England Humans Length of Stay Machine Learning Patient Discharge Decision Support Techniques Algorithms Electronic Health Records Female
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Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

PloS one Feb 22, 2019
BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data...
Infant Electronic Health Records Humans Diagnosis, Computer-Assisted Critical Care Female Sepsis Infant, Newborn Models, Biological Male Machine Learning Retrospective Studies
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Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.

Computers in biology and medicine Feb 16, 2019
BACKGROUND: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels.
Aged Male Middle Aged Treatment Outcome Biomarkers Consciousness Disorders Humans Prognosis Phosphopyruvate Hydratase Machine Learning Female Algorithms Bayes Theorem Critical Care Hypoxia-Ischemia, Brain
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