AIMC Topic: Intensive Care Units

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Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BMC medical informatics and decision making Sep 21, 2020
BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients' outcomes and data mining is such a powerf...
Humans Machine Learning Algorithms Acute Kidney Injury Intensive Care Units Electronic Health Records
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Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.

European radiology Sep 18, 2020
OBJECTIVE: To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.
Prognosis Retrospective Studies Tomography, X-Ray Computed Intensive Care Units Female Humans Artificial Intelligence COVID-19 Radiography, Thoracic Mortality Deep Learning Male Age Factors SARS-CoV-2 Middle Aged Radiography Hospitalization Italy Comorbidity Coronary Artery Disease Emergency Service, Hospital Aged Severity of Illness Index Sex Factors Pulmonary Disease, Chronic Obstructive Neurodegenerative Diseases Proportional Hazards Models
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Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

The Annals of pharmacotherapy Sep 15, 2020
INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship be...
Retrospective Studies Critical Illness Intensive Care Units Female Hospitalization Humans Machine Learning Middle Aged Hospital Mortality Bayes Theorem APACHE Medication Reconciliation Aged Aged, 80 and over Adult Male
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Data-driven ICU management: Using Big Data and algorithms to improve outcomes.

Journal of critical care Sep 9, 2020
The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" can be used to refer to the analysis of these datasets that collect enormous amount of data of differ...
Intensive Care Units Biomedical Research Big Data Data Mining Machine Learning Forecasting Retrospective Studies Humans
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Using Machine Learning to Make Predictions in Patients Who Fall.

The Journal of surgical research Aug 18, 2020
BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model base...
Humans Middle Aged Length of Stay Accidental Falls Female Injury Severity Score Machine Learning ROC Curve Decision Trees Algorithms Aged Aged, 80 and over Trauma Centers Patient Discharge Retrospective Studies Adult Glasgow Coma Scale Intensive Care Units Male
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Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

The Annals of otology, rhinology, and laryngology Aug 14, 2020
OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annot...
Ulcer Laryngoscopy Intubation, Intratracheal Larynx Granuloma, Laryngeal Respiration, Artificial Image Processing, Computer-Assisted Proof of Concept Study Airway Extubation Humans Neural Networks, Computer Machine Learning Intensive Care Units
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Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

Journal of critical care Jul 24, 2020
PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on...
Supervised Machine Learning Critical Care Early Diagnosis Adolescent ROC Curve Retrospective Studies Adult Humans Aged Respiratory Distress Syndrome Male Intensive Care Units Databases, Factual Young Adult Area Under Curve Middle Aged Female Risk Factors
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Prediction on critically ill patients: The role of "big data".

Journal of critical care Jul 23, 2020
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simpl...
Critical Care Humans Intensive Care Units Machine Learning Severity of Illness Index Electronic Health Records Delivery of Health Care Length of Stay Prognosis Hospital Mortality Critical Illness
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Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

Scientific reports Jul 3, 2020
We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitali...
Infant Severity of Illness Index Cohort Studies Regression Analysis Positive-Pressure Respiration Bronchiolitis Decision Trees Predictive Value of Tests Intensive Care Units Female Hospitalization Male Humans Machine Learning Prospective Studies
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Prediction of mortality in Intensive Care Units: a multivariate feature selection.

Journal of biomedical informatics May 23, 2020
CONTEXT: The critical nature of patients in Intensive Care Units (ICUs) demands intensive monitoring of their vital signs as well as highly qualified professional assistance. The combination of these needs makes ICUs very expensive, which requires in...
APACHE Humans Vital Signs Machine Learning Intensive Care Units Hospital Mortality
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