Sepsis

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study.

BACKGROUND: Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosis challenging. Traditional diagnostic tools are often limited, and machine learning techniques offer promising solutions for predicting adverse outcom...

ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.

BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). Existing prediction models for MAKE30 are static and overlook dynamic changes in clinical status. We introduce ORAKLE, a novel deep-learning model that utilizes evolving time-series data to predict MAKE30, enabling personalized, patient-cen...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study ai...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strat...

Clinical Validation of Deep Learning for Real-Time Tissue Oxygenation Estimation Using Spectral Imaging

Accurate, real-time monitoring of tissue ischemia is crucial to understand tissue health and guide surgery. Spectral imaging shows great potential f...

Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection.

By utilizing the synergistic effects of a dual-metal cobalt@copper electrode and advanced machine learning algorithms, we have developed a reliable an...

Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.

In recent years, the incidence of refractory Mycoplasma pneumoniae pneumonia (RMPP) has significantly risen, posing severe pulmonary and extrapulmonar...

Bio inspired feature selection and graph learning for sepsis risk stratification.

Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machin...

A Chemistry-Informed Generative Deep Learning Approach for Enhancing Voltammetric Neurochemical Sensing in Living Mouse Brain.

Exploring the time-resolved dynamics of neurochemicals is essential for deciphering neuronal functions, intercellular communication, and neurophysiolo...

Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term...

Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.

IMPORTANCE: Norepinephrine is the first-line vasopressor for patients with septic shock. When and whether a second agent, such as vasopressin, should ...

Prediction of bloodstream infection using machine learning based primarily on biochemical data.

Early diagnosis of bloodstream infection (BSI) is crucial for informed antibiotic use. This study developed a machine learning approach for early BSI ...

Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics.

Differential equations-based epidemiological compartmental systems and deep neural networks-based artificial intelligence can effectively analyze and ...

Enhancing Clinical Decision Support: A Heuristic Evaluation of Explainable AI in Healthcare Dashboards.

Explainable Artificial Intelligence (XAI) is crucial for enhancing transparency, interpretability and actionability of AI systems, particularly in hea...

An integrated bioinformatics and machine learning-based approach to depict key immunological players associated with candidemia during immunodeficiency.

It is evident that a robust immune system keeps Candida albicans infection in check, but weakened immunity opens the door for shifting from a benign y...

MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.

Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity o...

AI-based decoding of long covid cognitive impairments in mice using automated behavioral system and comparative transcriptomic analysis.

Long COVID (LC) following SARS-CoV-2 infection affects millions of individuals world-wide and manifests with a variety of symptoms including cognitive...

RNA polymerase III transcription-associated polyadenylation promotes the accumulation of noncoding retrotransposons during infection.

UNLABELLED: The accumulation of RNA Polymerase III (Pol III) transcribed short interspersed nuclear element (SINE) retrotransposon RNA is a hallmark o...

Assessing Healthcare Stakeholder Understanding of Machine Learning Documentation.

Artificial Intelligence (AI) has significantly advanced clinical decision support systems in healthcare, particularly using Machine Learning (ML) mode...

Transforming Healthcare: The Role of Artificial Intelligence.

The integration of artificial intelligence (AI) into healthcare is revolutionising the industry by enhancing diagnostic accuracy, personalising treatm...