AIMC Topic: Intensive Care Units

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Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit.

BMC medical research methodology Apr 24, 2023
BACKGROUND: The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addre...
Hospital Mortality APACHE Algorithms Intensive Care Units Machine Learning Humans
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Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.

BMC neurology Apr 22, 2023
INTRODUCTION: Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create...
Consensus Stroke Intensive Care Units Delphi Technique Critical Care Humans Artificial Intelligence
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Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction.

Computer methods and programs in biomedicine Apr 11, 2023
BACKGROUND AND OBJECTIVE: Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients w...
Machine Learning Algorithms Humans Survival Analysis Intensive Care Units Critical Care
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The Role of Data Science in Closing the Implementation Gap.

Critical care clinics Apr 7, 2023
Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment pa...
Intensive Care Units Data Science Critical Care Humans Artificial Intelligence
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The comforting companion: using AI to bring loved one's voices to newborns, infants, and unconscious patients in ICU.

Critical care (London, England) Apr 6, 2023
Voice Artificial Intelligence Intensive Care Units Infant, Newborn Infant Humans
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A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Journal of biomedical informatics Apr 5, 2023
Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-...
Machine Learning Databases, Factual Humans Area Under Curve Critical Care Intensive Care Units Hospital Mortality
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Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Scientific reports Mar 11, 2023
Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery ...
Humans Machine Learning Intensive Care Units Electronic Health Records Comorbidity Time Factors
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Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

International journal of environmental research and public health Feb 16, 2023
It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the IC...
Intensive Care Units Artificial Intelligence Patient Readmission Bayes Theorem Humans Machine Learning
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Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.

Critical care medicine Feb 15, 2023
OBJECTIVES: To predict impending delirium in ICU patients using recurrent deep learning.
Delirium Alberta Intensive Care Units Retrospective Studies Deep Learning Critical Illness Humans
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Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation.

Critical care (London, England) Jan 25, 2023
BACKGROUND: Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time ...
Intensive Care Units Radiography Humans Deep Learning Trachea Intubation, Intratracheal
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