AIMC Topic: Intensive Care Units

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Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability to predict HAP could improve patient outcomes and reduce costs. Traditional pneumonia risk predictio...
Risk Assessment Deep Learning Healthcare-Associated Pneumonia Humans Intensive Care Units Sensitivity and Specificity
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Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research.

Respiratory research Feb 7, 2020
BACKGROUND: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate ...
Intensive Care Units Female Aged Decision Trees Aged, 80 and over Respiratory Mechanics Biomedical Research Humans Pneumonia, Ventilator-Associated Male Machine Learning Electronic Nose Middle Aged Case-Control Studies
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Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Artificial intelligence in medicine Jan 30, 2020
After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical p...
Hospitals, Teaching Age Factors Emergency Service, Hospital Severity of Illness Index Humans Quality Improvement Time Factors Machine Learning Patient Transfer Logistic Models Critical Illness Models, Theoretical Intensive Care Units
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A machine-learning approach to predicting hypotensive events in ICU settings.

Computers in biology and medicine Jan 24, 2020
BACKGROUND: Predicting hypotension well in advance provides physicians with enough time to respond with proper therapeutic measures. However, the real-time prediction of hypotension with high positive predictive value (PPV) is a challenge. This is du...
Algorithms Intensive Care Units Predictive Value of Tests Hypotension Humans Machine Learning
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Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk.

Scientific reports Jan 24, 2020
To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Several ...
Risk Bayes Theorem Patient Readmission Odds Ratio Humans Disease Progression Medical Records Systems, Computerized Neural Networks, Computer Deep Learning Algorithms Chronic Disease Forecasting Communicable Diseases Intensive Care Units Sensitivity and Specificity
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Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.

PloS one Jan 10, 2020
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multi- modal biosignals. Most of the current work in the literature are eithe...
False Positive Reactions Clinical Alarms Humans Neural Networks, Computer Algorithms Intensive Care Units Sensitivity and Specificity Monitoring, Physiologic Supervised Machine Learning Arrhythmias, Cardiac
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Deep Natural Language Processing Identifies Variation in Care Preference Documentation.

Journal of pain and symptom management Jan 9, 2020
CONTEXT: Documentation of care preferences within 48 hours of admission to an intensive care unit (ICU) is a National Quality Forum-endorsed quality metric for older adults. Care preferences are poorly captured by administrative data.
Humans Retrospective Studies Aged, 80 and over Respiration, Artificial Natural Language Processing Documentation Intensive Care Units Female Aged
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Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.

Journal of critical care Jan 9, 2020
PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibility of a search based on natural language processing (NLP) on the text sections of electronic health r...
Sensitivity and Specificity Middle Aged Incidence Shock, Septic Hospitalization Young Adult Adult False Positive Reactions International Classification of Diseases Predictive Value of Tests Natural Language Processing Epidemiologic Studies Pattern Recognition, Automated Humans Software Databases, Factual Prospective Studies Intensive Care Units Electronic Health Records Female
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Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock - rationale and design of a multicenter randomized controlled superiority trial.

Trials Dec 18, 2019
BACKGROUND: Antibiotic exposure is often inadequate in critically ill patients with severe sepsis or septic shock and this is associated with worse outcomes. Despite markedly altered and rapidly changing pharmacokinetics in these patients, guidelines...
Dose-Response Relationship, Drug Multicenter Studies as Topic Randomized Controlled Trials as Topic Artificial Intelligence Drug Monitoring Research Design Netherlands Big Data Humans Female Anti-Bacterial Agents Shock, Septic Kaplan-Meier Estimate Adult Intensive Care Units Equivalence Trials as Topic Treatment Outcome Male Severity of Illness Index
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Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study.

BMJ open Dec 2, 2019
INTRODUCTION: About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic insta...
Decision Support Systems, Clinical California Electronic Health Records Postoperative Complications Hemodynamics Machine Learning Research Design Retrospective Studies Intensive Care Units Humans
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