AIMC Topic: Sepsis

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An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Scientific reports Aug 19, 2021
Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely d...
Machine Learning Algorithms Biomarkers Humans Case-Control Studies Sepsis Bayes Theorem Clinical Decision-Making Reproducibility of Results
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Kinematics approach with neural networks for early detection of sepsis (KANNEDS).

BMC medical informatics and decision making May 20, 2021
BACKGROUND: Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probab...
Biomechanical Phenomena Sepsis Vital Signs Early Diagnosis Humans Neural Networks, Computer
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Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

PloS one May 17, 2021
We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI Ar...
Sepsis Gene Expression Regulation Female Male Databases, Nucleic Acid Gene Expression Profiling Oligonucleotide Array Sequence Analysis Humans Machine Learning
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A predictive framework in healthcare: Case study on cardiac arrest prediction.

Artificial intelligence in medicine May 7, 2021
Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learni...
Heart Arrest Delivery of Health Care Sepsis Humans Machine Learning
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Can artificial intelligence and machine learning help us treat sepsis?

Intensive & critical care nursing May 4, 2021
Sepsis Artificial Intelligence Machine Learning Humans
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Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Clinical and translational science May 2, 2021
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center ...
Humans Machine Learning Prognosis Electronic Health Records Female Prospective Studies Biomarkers Early Diagnosis Middle Aged Hospital Mortality Aged Length of Stay Male ROC Curve Sepsis Emergency Service, Hospital Area Under Curve Patient Readmission
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Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.

Journal of medical Internet research Apr 22, 2021
BACKGROUND: Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the dev...
Sepsis Randomized Controlled Trials as Topic Humans Artificial Intelligence Risk Assessment
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Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review.

Intensive & critical care nursing Apr 17, 2021
BACKGROUND: Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and electronic health records allow for new opportunities to compute predictions based on machine learning techniques. There has been a lack of consensus iden...
Adult Sepsis Humans Machine Learning
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Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

International journal of medical informatics Apr 10, 2021
BACKGROUND AND OBJECTIVES: Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsi...
Humans Algorithms Clinical Decision-Making Sepsis Artificial Intelligence Machine Learning
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Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.

JAMA network open Apr 1, 2021
IMPORTANCE: Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. However, the atypical presentation of sepsis within this population makes detection challenging, and exi...
Machine Learning Decision Support Techniques Female Adult Retrospective Studies Humans Hematopoietic Stem Cell Transplantation Random Allocation Risk Assessment Immunocompetence Sepsis ROC Curve Male Middle Aged
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