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Intensive Care Units

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The Role of Data Science in Closing the Implementation Gap.

Critical care clinics
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...

A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Journal of biomedical informatics
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-...

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Scientific reports
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 ...

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

International journal of environmental research and public health
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...

Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.

Critical care medicine
OBJECTIVES: To predict impending delirium in ICU patients using recurrent deep learning.

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)
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 ...

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

Critical care medicine
OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill p...

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.

Sensors (Basel, Switzerland)
Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to sele...

Knowledge Graph Embeddings for ICU readmission prediction.

BMC medical informatics and decision making
BACKGROUND: Intensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterioration, and a financial burden for healthcare facilities. As healthcare became more data-driven with...