AIMC Topic: Intensive Care Units

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Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we t...
Observation Infusions, Intravenous Fluid Therapy Vasoconstrictor Agents Medical History Taking Retrospective Studies Humans Intensive Care Units Machine Learning Deep Learning Sepsis
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Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant. To determin...
Diagnostic Tests, Routine Information Seeking Behavior Medical Records Systems, Computerized Intensive Care Units Electronic Health Records Humans User-Computer Interface Machine Learning Respiratory Insufficiency Physicians Acute Kidney Injury
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An Interpretable ICU Mortality Prediction Model Based on Logistic Regression and Recurrent Neural Networks with LSTM units.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Most existing studies used logistic regression to establish scoring systems to predict intensive care unit (ICU) mortality. Machine learning-based approaches can achieve higher prediction accuracy but, unlike the scoring systems, frequently cannot pr...
Humans Databases, Genetic Hospitalization Neural Networks, Computer Risk Assessment Machine Learning Hospital Mortality Logistic Models Prognosis Intensive Care Units
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Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.

Scientific reports Nov 20, 2018
Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning...
Support Vector Machine Taiwan APACHE Airway Extubation Glasgow Coma Scale Middle Aged Hospital Mortality Female ROC Curve Aged Logistic Models Aged, 80 and over Male Humans Machine Learning Intensive Care Units
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Machine learning in critical care: state-of-the-art and a sepsis case study.

Biomedical engineering online Nov 20, 2018
BACKGROUND: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the dauntin...
Intensive Care Units Reproducibility of Results Sepsis Probability Software Data Analysis Prospective Studies Prognosis Critical Care Expert Systems Medical Informatics Computer Graphics Humans Artificial Intelligence Machine Learning
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Vitamin D in the ICU: More sun for critically ill adult patients?

Nutrition (Burbank, Los Angeles County, Calif.) Nov 16, 2018
Critical illness in patients is characterized by systemic inflammation and oxidative stress. Vitamin D has a myriad of biological functions relevant to this population, including immunomodulation by the alteration of cytokine production and nuclear f...
Humans Dietary Supplements Critical Illness Nutrition Therapy Intensive Care Units Vitamins Adult Hospital Mortality Vitamin D Vitamin D Deficiency
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Deep learning algorithms to identify documentation of serious illness conversations during intensive care unit admissions.

Palliative medicine Nov 14, 2018
Humans Algorithms Health Personnel Intensive Care Units Female Admitting Department, Hospital Critical Care Documentation Guidelines as Topic Adult Boston Male Middle Aged Deep Learning
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Optimal intensive care outcome prediction over time using machine learning.

PloS one Nov 14, 2018
BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions ...
Humans Machine Learning Prognosis Retrospective Studies Intensive Care Units Clinical Decision-Making Female Logistic Models Sepsis Feasibility Studies Hospital Mortality Critical Care Aged ROC Curve Datasets as Topic APACHE Male Decision Support Systems, Clinical Patient Admission Middle Aged Risk Assessment
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Applying machine learning to continuously monitored physiological data.

Journal of clinical monitoring and computing Nov 11, 2018
The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for moni...
Adult Respiration, Artificial Sepsis Decision Support Systems, Clinical Algorithms Databases, Factual Intensive Care Units Electronic Health Records Humans Machine Learning Atrial Fibrillation Software Monitoring, Physiologic Critical Care Expert Systems Randomized Controlled Trials as Topic Delirium Wavelet Analysis Arrhythmias, Cardiac Clinical Alarms
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Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks.

Artificial intelligence in medicine Nov 9, 2018
INTRODUCTION: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsi...
Humans Neural Networks, Computer Intensive Care Units Electronic Health Records Blood Culture
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