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Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.
Biomedicines
December 2024
Carol Davila University of Medicine and Pharmacy, Faculty of Medicine, General Surgery Department, University Emergency Hospital of Bucharest, 050098 Bucharest, Romania.
Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database.
View Article and Find Full Text PDFSepsis-Associated Acute Kidney Injury: What's New Regarding Its Diagnostics and Therapeutics?
Diagnostics (Basel)
December 2024
First Department of Internal Medicine, Sismanogleio General Hospital, 15126 Athens, Greece.
Sepsis-associated acute kidney injury (SA-AKI) is defined as the development of AKI in the context of a potentially life-threatening organ dysfunction attributed to an abnormal immune response to infection. SA-AKI has been associated with increased mortality when compared to sepsis or AKI alone. Therefore, its early recognition is of the utmost importance in terms of its morbidity and mortality rates.
View Article and Find Full Text PDFIdentification and experimental validation of diagnostic and prognostic genes CX3CR1, PID1 and PTGDS in sepsis and ARDS using bulk and single-cell transcriptomic analysis and machine learning.
Front Immunol
January 2025
Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Background: Sepsis is an uncontrolled reaction to infection that causes severe organ dysfunction and is a primary cause of ARDS. Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their simultaneous occurrence are unclear.
View Article and Find Full Text PDFInterpretable machine learning for predicting sepsis risk in emergency triage patients.
Sci Rep
January 2025
Department of Emergency, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, 11001, China.
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive triage information beyond vital signs. This retrospective cohort study utilized data from the MIMIC-IV database.
View Article and Find Full Text PDFPreparing Physicians of the Future: Incorporating Data Science into Medical Education.
Med Sci Educ
December 2024
Department of Internal Medicine, Pediatrics, and Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI USA.
The recent excitement surrounding artificial intelligence (AI) in health care underscores the importance of physician engagement with new technologies. Future clinicians must develop a strong understanding of data science (DS) to further enhance patient care. However, DS remains largely absent from medical school curricula, even though it is recognized as vital by medical students and residents alike.
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