AIMC Topic: Intensive Care Units

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EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinic...
Data Mining Information Storage and Retrieval Clinical Decision Rules Intensive Care Units Electronic Health Records Benchmarking Machine Learning Prognosis Humans
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Machine Learning Techniques for Personalized Detection of Epileptic Events in Clinical Video Recordings.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Continuous patient monitoring is essential to achieve an effective and optimal patient treatment in the intensive care unit. In the specific case of epilepsy it is the only way to achieve a correct diagnosis and a subsequent optimal medication plan i...
Electroencephalography Humans Video Recording Intensive Care Units Epilepsy Machine Learning Male Seizures Precision Medicine Monitoring, Physiologic
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A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated ...
Humans Mortality Neural Networks, Computer Intensive Care Units Critical Care
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[Artificial intelligence in neurocritical care].

Der Nervenarzt Jan 24, 2021
Artificial intelligence (AI) has been introduced into medicine and an AI-assisted medicine will be the future that we should help to shape. In particular, supervised, unsupervised, and reinforcement learning will be the main methods to play a role in...
Humans Artificial Intelligence Monitoring, Physiologic Critical Care Prospective Studies Intensive Care Units
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Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

European radiology Jan 21, 2021
OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data.
Tomography, X-Ray Computed Intensive Care Units Adult Length of Stay Humans Machine Learning Retrospective Studies Torso
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Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jan 20, 2021
OBJECTIVE: To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 2019 (COVID-19) patients receiving neuromuscular blocking agent.
Humans Female Aged Male Middle Aged Machine Learning COVID-19 Intensive Care Units Temporal Lobe COVID-19 Drug Treatment Anesthesia Frontal Lobe Cohort Studies Hypnotics and Sedatives Electroencephalography
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Interpreting a recurrent neural network's predictions of ICU mortality risk.

Journal of biomedical informatics Jan 7, 2021
Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. Algorithms such as Recurrent Neural Networks (RNNs) when applied to Ele...
Algorithms Intensive Care Units Electronic Health Records Child Humans Neural Networks, Computer
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A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

PloS one Dec 17, 2020
BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identification of and decision-making for patients showing signs of developing physiological instability during treatment. The aim of this study was to enhance t...
Acid-Base Equilibrium Risk Assessment Deep Learning Electrolytes Female Adult Male Humans Retrospective Studies Intensive Care Units
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Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.

Journal of women's health (2002) Dec 16, 2020
The impact of sex and gender in the incidence and severity of coronavirus disease 2019 (COVID-19) remains controversial. Here, we aim to describe the characteristics of COVID-19 patients at disease onset, with special focus on the diagnosis and mana...
Humans Machine Learning Electronic Health Records Female COVID-19 Intensive Care Units Natural Language Processing Sex Factors Spain Adult Middle Aged SARS-CoV-2 Male Sex Distribution Adolescent Hospitalization Young Adult
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Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study.

Respiratory research Dec 10, 2020
BACKGROUND: Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine le...
Health Status Predictive Value of Tests Ventilator-Induced Lung Injury Respiration, Artificial Lung Treatment Outcome Reproducibility of Results Risk Factors Aged Critical Illness Machine Learning Middle Aged Intensive Care Units Decision Support Techniques Risk Assessment Female Phenotype Adult Humans Databases, Factual Male Retrospective Studies
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