Critical Care

A Generative Multi-modal Deep Learning Framework for Non-invasive Prediction of Ki-67 Expression in Papillary Thyroid Microcarcinoma Using Preoperative Ultrasound.

Accurate assessment of Ki-67 expression plays a critical role in the risk stratification and prognosis of papillary thyroid microcarcinoma (PTMC). However, its clinical evaluation still relies on invasive biopsy. To address this limitation, we propose a novel generative multi-modal and multi-task learning framework for non-invasive prediction of Ki-67 expression using only preoperative B mode ultr...

Machine Learning-Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study.

BACKGROUND: Effective risk stratification in sepsis remains a critical clinical challenge. Serum lactate is a cornerstone biomarker of metabolic dysfunction, yet its predictive limitations-particularly in patients without severe hyperlactatemia-are well recognized. The lactate-to-albumin ratio (LAR), a composite mixed-unit index integrating markers of acute metabolic dysfunction and systemic infla...

Physics-Informed LSTM Neural Network for Personalized Pharmacokinetic Modeling of Polymyxin B in Critically Ill Patients.

Polymyxin B represents a critical last-resort antibiotic for carbapenem-resistant Gram-negative infections, yet its narrow therapeutic window and subs...

A Multimodal Deep Learning Model for Preoperative Prediction of Postoperative Complications in Gastric Cancer.

BACKGROUND: Clavien-Dindo (CD) grade ≥II complications occur in roughly one in five patients after curative gastrectomy for gastric cancer and indepen...

A Deterministic Framework for Transforming Multi-Informant Observational Data into Structured Descriptors: A Feasibility Study.

Background Multi-informant observational data obtained from parents and educators provide rich but context-dependent information. However, these data ...

Acute kidney injury severity in ICU patients: Developing and evaluating a data-driven analysis of clinical covariates using machine learning.

BACKGROUND: Acute kidney injury remains a major cause of morbidity and mortality in critically ill patients. Existing classification systems rely on s...

Simulator development using natural language: clinician-led innovation through artificial intelligence.

BACKGROUND: Healthcare simulation training faces significant barriers due to the "clinician-developer gap," where educators lack programming expertise...

Machine Learning-Based Early Risk Stratification for Sepsis-Related Troponin-Defined Myocardial Injury Using Routine Metabolic Indicators.

BACKGROUND: Early recognition of sepsis-related myocardial injury during sepsis remains difficult, partly because harmonized echocardiographic phenoty...

Leashing heterogeneous sepsis with phenotype-informed decision support modeling.

Detection of bronchopulmonary dysplasia in infants and prediction of school-age lung function from tidal breathing data using recurrent neural networks.

Objective.To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and witho...

Biomarkers in predicting mortality in hemodialysis patients: recent advances.

Patients undergoing maintenance hemodialysis face annual mortality rates of 15-27%, with cardiovascular causes accounting for more than half of all de...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Enhanced MobileNet with multi-scale feature fusion for automated breast cancer histopathology classification.

Accurate and efficient diagnosis of breast cancer from histopathological images remains a major challenge in clinical practice due to subjective inter...

Optimizing stroke mortality prediction: a comparative study of sampling, cost-sensitive learning, and threshold adjustment.

BACKGROUND: Stroke is a leading cause of mortality and disability worldwide, creating a critical need for accurate prediction tools to support early c...

Machine learning-optimized bioinspired N-doped carbon-wrapped trimetallic oxides for an efficient oxygen evolution reaction.

Despite the significant advancement in the development of a wide range of nitrogen-doped multi-metal oxide-based electrocatalysts for the oxygen evolu...

An Interactive Virtual Nephrologist to Engage Patients With Advanced CKD About Transplantation.

RATIONALE & OBJECTIVE: Despite advantages in survival and quality of life with kidney transplantation (KT) compared to other treatments for kidney fai...

An interpretable machine learning model for early prediction of ESBL-producing bacteraemia in the emergency department.

Empirical antibiotic therapy in the emergency department (ED) is often initiated before susceptibility results are available, risking treatment failur...

Fairness in multimodal machine learning applications in clinical decision support: a systematic review.

Multimodal AI models are increasingly used in clinical decision support systems, but fairness appears to be rarely measured. In this systematic review...

Open dataset and deep learning model for intelligent diagnosis of neonatal respiratory distress syndrome and aspiration syndrome in newborns.

Neonatal pulmonary diseases such as aspiration syndrome (AS) and respiratory distress syndrome (NRDS) require timely diagnosis, yet manual interpretat...

Association Between the Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Perioperative Mortality Risk in Myasthenia Gravis Patients with Sepsis: A Retrospective Cohort Study.

BackgroundThis study aims to explore the association between the Hemoglobin/Red Cell Distribution Width Ratio (HRR) and perioperative mortality in mya...