AIMC Topic: Critical Illness

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Predicting brain function status changes in critically ill patients via Machine learning.

Journal of the American Medical Informatics Association : JAMIA Oct 12, 2021
OBJECTIVE: In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hosp...
Brain Critical Illness Intensive Care Units Humans Machine Learning Prospective Studies Adult
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Algorithmic prognostication in critical care: a promising but unproven technology for supporting difficult decisions.

Current opinion in critical care Oct 1, 2021
PURPOSE OF REVIEW: Patients, surrogate decision makers, and clinicians face weighty and urgent decisions under uncertainty in the ICU, which could be aided by risk prediction. Although emerging artificial intelligence/machine learning (AI/ML) algorit...
Technology Prospective Studies Critical Illness Critical Care Humans Artificial Intelligence
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Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Shock (Augusta, Ga.) Jul 1, 2021
BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically i...
Critical Illness Intensive Care Units Female Humans Retrospective Studies Deep Learning Cohort Studies Models, Theoretical Vasoconstrictor Agents Aged Male Middle Aged Vital Signs Needs Assessment
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Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

Shock (Augusta, Ga.) Jul 1, 2021
BACKGROUND: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a ...
Critical Illness Female Aged Sepsis Time Factors Male Artificial Intelligence Middle Aged Prognosis Retrospective Studies Humans
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[Application of the artificial intelligence-rapid whole-genome sequencing diagnostic system in the neonatal/pediatric intensive care unit].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics May 1, 2021
Pediatric patients in the neonatal intensive care unit (NICU) and the pediatric intensive care unit (PICU) have a high incidence rate of genetic diseases, and early rapid etiological diagnosis and targeted interventions can help to reduce mortality o...
Intensive Care Units, Neonatal Humans Artificial Intelligence Critical Illness Intensive Care Units, Pediatric Whole Genome Sequencing Infant, Newborn Child
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Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: To apply natural language processing (NLP) techniques to identify individual events and modes of communication between healthcare professionals and families of critically ill patients from electronic medical records (EMR).
Datasets as Topic Family Professional-Family Relations Intensive Care Units Communication Natural Language Processing Retrospective Studies Critical Illness Humans Machine Learning
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Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.

Current opinion in critical care Dec 1, 2020
PURPOSE OF REVIEW: Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisti...
Acute Kidney Injury Humans Critical Illness Artificial Intelligence Intensive Care Units Renal Replacement Therapy
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[Artificial intelligence provides promotion of big data in medical work and contribution to people's health as soon as possible: real-time warning of critical illness is the pioneer of artificial intelligence in clinical medicine].

Zhonghua wei zhong bing ji jiu yi xue Oct 1, 2020
Through the big data intelligent algorithm and application of artificial intelligence in critically ill patients, the value of the combination of clinical real-time warning and artificial intelligence in critical care medicine was explored. Artificia...
Artificial Intelligence Critical Illness Big Data Humans Critical Care
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A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.

The journal of trauma and acute care surgery Oct 1, 2020
BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We...
Middle Aged Sepsis ROC Curve Proportional Hazards Models Machine Learning Multivariate Analysis Prognosis Sodium Humans Retrospective Studies Young Adult Injury Severity Score Critical Care Neural Networks, Computer Intensive Care Units Critical Illness Adult Organ Dysfunction Scores Female Male Wounds and Injuries Hospitalization
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Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

Critical care medicine Jul 1, 2020
OBJECTIVES: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies.
Radiography, Thoracic Female Supine Position Humans Retrospective Studies Lung Diseases Reproducibility of Results Artificial Intelligence Middle Aged Tomography, X-Ray Computed Image Interpretation, Computer-Assisted Male Algorithms Critical Illness Radiologists
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