AIMC Topic: Sepsis

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Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

British journal of anaesthesia Jan 16, 2021
BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthor...
Prospective Studies Critical Illness Female Aged Humans Machine Learning Sepsis Shock, Septic Predictive Value of Tests Fluid Therapy Male Point-of-Care Systems Echocardiography Middle Aged
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Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

Journal of translational medicine Dec 7, 2020
BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible predicti...
Logistic Models Humans Machine Learning Sepsis Hospital Mortality ROC Curve
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Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

BMC cancer Nov 13, 2020
BACKGROUND: Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence o...
Neutropenia Support Vector Machine Follow-Up Studies Hematopoietic Stem Cell Transplantation Male Neoplasms Infant Sepsis Adolescent Child, Preschool Child Bacteremia Prognosis Retrospective Studies Female Infant, Newborn Humans Machine Learning
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SSP: Early prediction of sepsis using fully connected LSTM-CNN model.

Computers in biology and medicine Nov 10, 2020
BACKGROUND: Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote ...
Vital Signs Humans Neural Networks, Computer Machine Learning Intensive Care Units Sepsis
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Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.

EBioMedicine Nov 10, 2020
BACKGROUND: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subcla...
Humans Algorithms Biomarkers Transcriptome Deep Learning Sepsis ROC Curve Disease Susceptibility Prognosis Computational Biology Female Cluster Analysis Male Predictive Value of Tests Gene Expression Profiling
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Artificial Intelligence in the Intensive Care Unit.

Seminars in respiratory and critical care medicine Nov 5, 2020
The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definit...
Intensive Care Units Humans Electronic Health Records Artificial Intelligence Sepsis Machine Learning
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A deep learning approach for sepsis monitoring via severity score estimation.

Computer methods and programs in biomedicine Oct 28, 2020
BACKGROUND AND OBJECTIVE: Sepsis occurs in response to an infection in the body and can progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis, which is time-consuming, costly and requires medically trained personne...
Organ Dysfunction Scores Intensive Care Units Deep Learning Humans Sepsis Prognosis ROC Curve Retrospective Studies
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Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BMC medical informatics and decision making Oct 27, 2020
BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse...
Sepsis Humans Time Factors Machine Learning Hospital Mortality Predictive Value of Tests Forecasting Algorithms Reproducibility of Results Decision Support Systems, Clinical Datasets as Topic Retrospective Studies Male Time-to-Treatment Intensive Care Units Severity of Illness Index Female
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Not All Sepsis-Associated Acute Kidney Injury Is the Same: There May Be an App for That.

Clinical journal of the American Society of Nephrology : CJASN Oct 8, 2020
Acute Kidney Injury Humans Deep Learning Sepsis Mobile Applications
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Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

Clinical journal of the American Society of Nephrology : CJASN Oct 8, 2020
BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.
Aged Male Middle Aged Comorbidity Deep Learning Sepsis Renal Dialysis United States Prognosis Acute Kidney Injury Electronic Health Records Female Humans Phenotype Databases, Factual Leukocyte Count Simplified Acute Physiology Score Glutamyl Aminopeptidase Bilirubin Creatinine Lactic Acid L-Lactate Dehydrogenase Liver Diseases Alanine Transaminase Blood Urea Nitrogen
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