Critical Care

Automated estimation of drain output in postoperative patients using deep learning on clinical images.

Postoperative drains are essential components of care in general surgery and intensive care units, where accurate monitoring of drain output is critical for detecting complications such as hemorrhage, anastomotic leakage, or infection. Despite its importance, output measurement is still performed manually, which is time-consuming, exposes staff to biohazardous fluids, and is prone to documentation...

Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients.

Bloodstream infections require timely and appropriate diagnosis and treatment, as inadequate management is associated with higher mortality. Previous predictive models for bloodstream infection have generally incorporated laboratory test results in addition to vital signs, although laboratory results are not always immediately available in clinical practice. This study aimed to develop and validat...

Association between platelet-albumin-bilirubin score and 30-day in-hospital mortality in patients with sepsis: evidence from a large database and machine learning modeling.

A multimodal fusion network for heart sound abnormality detection and classification.

OBJECTIVE: Accurate physiological assessment of cardiac function from heart sounds remains challenging due to background noise, variable heart rat...

Emerging technologies and AI-assisted tools in cardiopulmonary monitoring.

PURPOSE OF REVIEW: Cardiopulmonary monitoring is fundamental to critical care, yet traditional approaches rely on simplified thresholds that capture o...

Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal dialysis.

Peritoneal dialysis (PD) is a common treatment for end-stage renal disease, yet cardiovascular disease (CVD) remains a major cause of morbidity. Inade...

Early-stage anomaly discrimination in lithium-ion batteries using internal pressure-temperature coupling and multi-source data fusion.

Thermal runaway is one of the most critical safety risks in lithium-ion batteries. Mitigating this risk requires not only detecting imminent failures ...

Predicting amyotrophic lateral sclerosis stage based on multi-parameter ultrasound: development and validation of an interpretable machine learning model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks sensitive, objective staging tools to guide clinical management and trials. Existing methods hav...

Large language model-based paper classification framework with key-insight extraction and confidence-weighted voting.

Systematic reviews (SRs) are critical for evidence-based research but are time-consuming and labor-intensive. The rapid expansion of academic publicat...

Lightweight large language models for early sepsis prediction via a semantic abstraction rule engine.

Sepsis remains a major global health challenge due to its high mortality rate and the difficulty of predicting its onset from nonspecific early sympto...

LSTM-GRU hybrid model for multi-layer microclimate prediction in solar greenhouse.

Precise microclimate control across vertical canopy layers is critical for optimizing crop production in horticulture solar greenhouses, yet existing ...

Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis.

Age is a well-known risk factor to develop severe viral respiratory infections, including severe COVID-19. This study aimed to identify the biological...

Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.

Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and ac...

Physiological predictors of respiratory motor plasticity: A machine-learning reappraisal of phrenic motor facilitation.

Acute intermittent hypoxia (AIH)-induced phrenic long-term facilitation (pLTF) is a well-established form of respiratory motor plasticity and a subtyp...

Machine learning-based precision subtyping and risk prediction in sepsis: a retrospective analysis using MIMIC-IV database.

Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.

BACKGROUND: Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LL...

Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification.

Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection method...

Artificial intelligence-based prediction of radio-cephalic arteriovenous fistula maturation using preoperative duplex examination: a retrospective multicenter cohort study.

Establishing a functional arteriovenous fistula (AVF) is crucial to ensure effective dialysis treatment. However, there are currently no definitive cr...

Artificial intelligence for monitoring hand hygiene compliance in healthcare settings: A scoping review.

BACKGROUND: Hand hygiene is a fundamental measure for preventing healthcare-associated infections, yet traditional monitoring methods are significantl...

Rhythms of mood: Bidirectional regulation of molecular clocks and neurotransmission in affective disorders.

Mood disorders, primarily major depressive and bipolar disorder, are characterized by significant neurochemical dysregulation and disturbances in biol...