Intensivists

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may stem from the absence of effective prognostic models. We developed a machine learning (ML)-based prognostic model for SALI using conventional biomarkers to guide precise clinical interventions and reduce mortality. METHODS: We retrospectively analyzed 3...

A machine learning model to identify pulmonary embolism in patients admitted to intensive care.

BACKGROUND: Pulmonary embolism (PE) is a leading cause of preventable death, yet statistical prediction models have shown inconsistent validity. Our primary objective was to determine if a machine learning model trained with data routinely collected in clinical care can successfully identify acute PE in critically ill patients. METHODS: Leveraging two multicenter datasets acquired nationally (deve...

Beyond handcrafted radiomics in oncologic imaging: Innovations in deep, explainable, multi-site and multi-omics radiomics approaches.

Radiomics seeks to convert medical images into quantitative biomarkers capable of capturing tumor phenotype, microenvironment, and underlying biology....

Interpretable four-factor day-1 nomogram for predicting sepsis-associated encephalopathy in septic ICU patients with AKI: Development and internal validation in MIMIC-IV.

Sepsis-associated encephalopathy (SAE) is common in the intensive care unit (ICU) and portends worse short- and long-term outcomes. To enable real-tim...

Artificial Intelligence-Assisted Point-of-Care Ultrasound for Evaluating Left Ventricular Ejection Fraction: A Systematic Review of Prospective Observational Studies.

Artificial intelligence (AI) embedded in point-of-care ultrasound (POCUS) could reduce operator dependence in left ventricular ejection fraction (LVEF...

Architecting Synergy: Knowledge Graphs for Representing Complex, Multi-Domain Patient Information.

Healthcare systems exchange more data than ever, yet gaps in care persist: missed referrals, unsafe polypharmacy, and loss of continuity. This paper a...

Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.

To synthesize and critically appraise applications of machine learning (ML) in pediatric cardiac intensive care, focusing on algorithm performance, va...

Development and evaluation of a prediction model for adult ICU hemorrhage using only continuous cardiorespiratory data.

Develop and evaluate whether a model trained to detect the physiological signature of hemorrhage in ICU patients generalizes to other cohorts. App...

Emerging Artificial Intelligence Technologies for Evaluation of Dental Composite Restorations: A Scoping Review.

Artificial intelligence (AI) is increasingly applied in restorative dentistry, but its role in assessing dental composite restorations is not yet well...

Artificial intelligence for sepsis management in the emergency department.

Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.

PURPOSE OF REVIEW: Maternal morbidity and mortality remain largely preventable, yet current risk-assessment tools identify only a fraction of women wh...

Machine learning for hemodynamic instability prediction and hemorrhage management in trauma and perioperative care.

PURPOSE OF REVIEW: Hemodynamic instability and uncontrolled hemorrhage remain leading causes of preventable morbidity and mortality in trauma and peri...

Rule-Based Protein Classification through Multi-Phase Feature Extraction Technique.

Protein sequence classification is a fundamental step toward functional annotation and biological analysis; however, most of the existing approaches r...

Practical machine learning model for early and accurate prediction of disseminated intravascular coagulation before its progression to an overt stage.

BACKGROUND AND AIMS: In patients with sepsis, anticoagulant therapy is expected to have maximal efficacy when administered before the development of s...

Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients.

PURPOSE OF REVIEW: Post-traumatic care is evolving from a reactive, protocol-driven paradigm to a predictive, personalized approach. This review exami...

Plasma metabolomic signatures in patients with multidrug-resistant bacterial sepsis.

BACKGROUND AND OBJECTIVE: Multidrug-resistant (MDR) bacterial infections are a leading cause of sepsis-related death. A rapid method to identify patie...

Development and internal validation of machine learning in predicting prognosis of acute kidney injury patients in resource-limited setting.

BACKGROUND: Machine learning models for predicting acute kidney injury (AKI) prognosis have primarily been developed in resource-rich settings, with l...

Development of a rapid metal oxide semiconductor-based sensory system for noninvasive neonatal sepsis detection.

Systemic immune-inflammation (SII) index as a novel prognostic biomarker in critically ill patients with sepsis: analysis of the MIMIC-IV cohort and predictive modeling based on machine learning.

Application of machine learning for the diagnosis and prognosis of sepsis-induced acute respiratory distress syndrome: a systematic review and meta-analysis.