Hematology

White cell - platelet ratio: A strong indicator for early mortality in liver cirrhosis patients with esophagogastric varices.

Esophagogastric varices (EGV) in liver cirrhosis patients within the intensive care unit (ICU) is a significant medical concern. This study aims to develop and validate a machine learning (ML) model to predict the early mortality of those patients. Medical information was extracted from Intensive Care (MIMIC)-IV database, and 793 cirrhotic patients accompanied with EGV were enrolled, randomly assi...

Development and external validation of a machine learning prediction model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in children using routine blood parameters: a retrospective cohort study.

BACKGROUND: Epstein-Barr virus (EBV) infection is a common pediatric infectious disease. Infectious mononucleosis (IM) and hemophagocytic lymphohistiocytosis (HLH), two major complications of EBV infection, share similar clinical manifestations in the early stage. While IM is typically self-limiting, HLH is life-threatening and requires immediate intervention. Early differentiation between these t...

Machine learning models to predict ICU admission of patients with COPD and depression: A retrospective cohort study.

This study aimed to develop and validate a machine learning (ML)-based predictive model to identify risk factors associated with intensive care unit (...

First-Pass Reperfusion After Endovascular Thrombectomy: A Real-World Analysis with Explainable Machine Learning for Intra-Procedural Decision Support.

OBJECTIVE: To evaluate determinants of first-pass reperfusion and to develop an explainable machine learning framework for intra-procedural decision s...

Risk prediction models based on machine learning for emergence delirium in elderly patients undergoing spine surgery: development and validation study.

BACKGROUND: Emergence delirium (ED) is a common complication in elderly patients undergoing surgery for degenerative spinal disease (DSD) and is assoc...

Integrated NEK7-inflammasome and platelet transcriptomic signature generates mechanistic hypotheses in heart failure.

BACKGROUND: The relationship between NEK7-NLRP3 inflammasome activation and elevated platelet activity in the progression of heart failure (HF) is not...

Detection of non-invasive sexing of early chick embryos in intact eggs using laser speckle contrast imaging and deep neural networks.

The ability to image blood flow in early-stage avian embryos has significant applications in developmental biology, drug and vaccine testing, as well ...

Application value and challenges associated with plasma cell-free DNA metagenomic sequencing technology in the diagnosis of infections in patients with hematological disorders.

In patients with hematological disorders, the high risk of complex infections caused by immune dysfunction and intensive therapies poses a major chall...

AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy.

Chimeric antigen receptor (CAR) T cells have demonstrated curative potential in hematologic cancers and increasing efficacy in solid tumors and non-ma...

Artificial intelligence in the prediction of intraoperative red blood cell transfusion in cardiac surgery: a systematic review and diagnostic test accuracy meta-analysis.

BACKGROUND: Red Blood Cell (RBC) transfusion in cardiac surgery is associated with risks. Conventional prediction scores lack accuracy, conflicting wi...

Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.

Accurate prognostication in the intensive care unit (ICU) is essential for delivering personalized and ethically sound care, yet it remains a challeng...

Compartmentalized biomarker correlations in HIV: plasma-CSF relationships vary by blood-brain/blood-CSF barrier permeability and viral suppression.

Neuroinflammation is common in people with HIV (PWH) and may be reflected also in plasma biomarkers; the latter are sometimes used as surrogates for C...

Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.

BACKGROUND: Machine learning (ML) shows promise in using clinical data to predict chronic diseases. However, its application in PMOP risk assessment u...

Development and validation of an interpretable machine learning model for early hospital-based differentiation of chikungunya and dengue fever using routine clinical data.

BACKGROUND: Chikungunya fever (CHIKF) and dengue fever are mosquito-borne viral diseases. These infections often circulate in the same regions at the ...

Machine Learning-Based Prediction of Dental Caries in 12-Year-Old Adolescents: A Nationwide Cross-Sectional Study in Korea.

OBJECTIVE: We aimed to develop and evaluate machine learning models to support population-level risk stratification for dental caries in the permanent...

A Multi-Task Segmentation and Classification Network Based on Ultrasound Images for Predicting the Grading of Ascites in the Abdominal Cavity.

Abdominal trauma with bleeding is a leading cause of post-traumatic death, and detecting free fluid in the abdomen or hemoperitoneum can provide criti...

Development of an Interpretable Machine Learning Model for Predicting Clavien-Dindo Grade ≥2 Complications after Unilateral Minimally Invasive Pyeloplasty in UPJO: A Retrospective Cohort Study.

BACKGROUND: Minimally invasive pyeloplasty (MIP), encompassing both conventional laparoscopy and robot-assisted approaches, has become the primary tre...

Comparison of Gram stain interpretation accuracy between a computer-aided diagnosis app and microbiology specialists in blood culture samples.

UNLABELLED: Gram staining is one of the basic tests to identify the organism in microbiological laboratory, especially in blood culture. However, micr...

Graph informed biomarker discovery framework using transcriptomic machine learning for glioblastoma prognosis.

Identifying reproducible, interpretable prognostic signals from high-dimensional transcriptomics remains challenging because gene-level models often i...

A machine learning framework for multimodal temporal prediction of neurological outcome after out-of-hospital cardiac arrest.

Neurological prognostication after out-of-hospital cardiac arrest (OHCA) remains challenging. Existing clinical scores rely on static, single-timepoin...