AIMC Topic: Intensive Care Units

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A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients.

Frontiers in public health
Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients...

Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.

Computational and mathematical methods in medicine
In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begi...

Artificial intelligence forecasting mortality at an intensive care unit and comparison to a logistic regression system.

Einstein (Sao Paulo, Brazil)
OBJECTIVE: To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to a recent logistic regression system in the literature, an...

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Scientific reports
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated becaus...

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

Critical care (London, England)
BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine le...

Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an important diagnostic tool because of its association with comas of various etiologies, as with hypoxia, drug related intoxication or deep anesthesia. ...

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

Scientific reports
In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of ...

Interpretability of time-series deep learning models: A study in cardiovascular patients admitted to Intensive care unit.

Journal of biomedical informatics
Interpretability is fundamental in healthcare problems and the lack of it in deep learning models is currently the major barrier in the usage of such powerful algorithms in the field. The study describes the implementation of an attention layer for L...