AIMC Topic: 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...
Humans Machine Learning Decision Support Techniques Critical Care Expert Systems Pattern Recognition, Automated False Positive Reactions Clinical Alarms Retrospective Studies Intensive Care Units Monitoring, Physiologic Knowledge Bases
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Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems.

Critical care clinics Apr 6, 2019
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities ...
Decision Support Systems, Clinical Telemedicine Humans Artificial Intelligence Intensive Care Units Big Data
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An attention based deep learning model of clinical events in the intensive care unit.

PloS one Feb 13, 2019
This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocardial infarction (MI), and vancomycin antibiotic administration over two week patient ICU courses in ...
Vancomycin Deep Learning Sepsis Myocardial Infarction Models, Biological Retrospective Studies Intensive Care Units Clinical Decision-Making Diagnosis, Computer-Assisted Humans Anti-Bacterial Agents
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DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning.

Scientific reports Feb 12, 2019
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging a...
Humans Databases, Factual Retrospective Studies Critical Illness Intensive Care Units Length of Stay Adolescent Hospital Mortality Longitudinal Studies Probability Critical Care Male Organ Dysfunction Scores Female Middle Aged Decision Making Aged Severity of Illness Index Area Under Curve Young Adult Deep Learning Adult
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Can AI Help Reduce Disparities in General Medical and Mental Health Care?

AMA journal of ethics Feb 1, 2019
BACKGROUND: As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve ...
Adult Male Middle Aged Mental Health Services Intensive Care Units Female Aged Aged, 80 and over Healthcare Disparities Sex Factors Patient Readmission Mortality Humans Artificial Intelligence Delivery of Health Care
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Artificial intelligence in the intensive care unit.

Critical care (London, England) Jan 10, 2019
Humans Artificial Intelligence Decision Support Techniques Intensive Care Units Critical Care
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Role of interleukin-3 as a prognostic marker in septic patients.

Revista Brasileira de terapia intensiva Jan 10, 2019
OBJECTIVE: To evaluate the accuracy of IL-3 to predict the outcome of septic patients.
Sensitivity and Specificity Male Female Middle Aged Shock, Septic Reproducibility of Results Humans Cohort Studies Sepsis Aged Predictive Value of Tests Prospective Studies Hospital Mortality Adult Interleukin-3 Prognosis Intensive Care Units
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Big Data Analysis and Machine Learning in Intensive Care Units.

Medicina intensiva Dec 24, 2018
Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine Learning (ML), due to the huge amount of information processed and stored in electronic format in relation to such care. These tools can improve our clinical re...
Big Data Randomized Controlled Trials as Topic Natural Language Processing Data Anonymization Information Storage and Retrieval Data Analysis Humans Databases, Factual Critical Care Machine Learning Retrospective Studies Information Dissemination Intensive Care Units Electronic Health Records
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A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.

International journal of medical informatics Dec 10, 2018
PURPOSE: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.
Aged, 80 and over Young Adult Adult Humans Male Sepsis Adolescent Critical Illness Blood Pressure Cardiovascular Diseases Machine Learning Intensive Care Units Middle Aged Algorithms Female Heart Rate Biomarkers Aged Models, Cardiovascular Retrospective Studies
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Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

JAMA network open Dec 7, 2018
IMPORTANCE: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, usin...
Severity of Illness Index Aged, 80 and over Electronic Health Records Natural Language Processing Humans Models, Statistical Adult Female Critical Care Outcomes Male Retrospective Studies Reproducibility of Results Middle Aged Critical Illness Aged Intensive Care Units
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