AIMC Topic: Sepsis

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Evaluating the Accuracy of Medical Information Generated by ChatGPT and Gemini and Its Alignment With International Clinical Guidelines From the Surviving Sepsis Campaign: Comparative Study.

JMIR formative research
BACKGROUND: Assessment of medical information provided by artificial intelligence (AI) chatbots like ChatGPT and Google's Gemini and comparison with international guidelines is a burgeoning area of research. These AI models are increasingly being con...

Early prediction of vasopressor initiation in ICU sepsis patients using an interpretable EHR-based ML model.

BMC medical informatics and decision making
BACKGROUND: Early identification of septic patients who will require vasopressor support could provide a critical window for hemodynamic optimisation, yet current bedside cues often appear only when shock is imminent.

Molecular mechanisms of lipid metabolism abnormalities driving sepsis and atrial fibrillation: A Systematic study based on bioinformatics and machine learning.

PloS one
BACKGROUND: Sepsis and atrial fibrillation are complex, life-threatening medical conditions affecting approximately 49 million individuals globally, characterized by exceptionally high mortality rates. Lipid metabolism abnormalities play a critical r...

Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department.

BMC emergency medicine
BACKGROUND: Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial int...

Construction of a Minimal Sensor Array Using Fingerprint Protein Corona on Nanostars for Detecting Protein Isoforms and Disease States.

ACS nano
Signature-based protein detection coupled with machine learning algorithms has revolutionized traditional sensing methods, providing rapid, inexpensive, and selectivity-driven detection without the use of specialized equipment. This strategy leverage...

Machine learning glucose forecasting models for septic patients.

Scientific reports
Sepsis-induced glucose fluctuations present major challenges in critical care, underscoring the importance of accurate glucose monitoring and forecasting to improve patient outcomes. This study introduces a suite of forecasting models trained using c...

Nanosensor-Based Pattern-Generating Probe Accelerates Sepsis Diagnosis.

ACS nano
Biomimetic optical sensor arrays hold promising potential in differentiating nuances within intricate mixtures and biosamples. Nonetheless, developing a standalone pattern-generating sensor for multianalyte identification in clinical biofluids, witho...

Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.

Annals of medicine
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...

Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...

The ferroptosis-related gene MAFG screened by machine learning is associated with the diagnosis and prognosis of sepsis.

Clinical and experimental medicine
Ferroptosis is a novel form of cell death induced by ferrous ions and lipid peroxidation. However, the mechanisms of ferroptosis-related genes (FRGs) in sepsis have not been studied thoroughly. We performed differential analysis using GSE65682, and t...