Hematology

A Retrospective Cohort Study on 28-Day Mortality in Immunosuppressed Sepsis: An Interpretability-Based Predictive Model Using MIMIC-IV V2.2.

BACKGROUND: Sepsis in immunosuppressed patients is associated with significantly higher mortality rates, yet predictive models tailored to this high-risk population remain limited. This study aims to develop an interpretable machine learning model to predict 28-day mortality in immunosuppressed sepsis patients, with a focus on model transparency and clinical applicability. METHODS: A retrospective...

Application of Convolutional Neural Network Image Analysis and Machine Learning to Basic Blood Tests for Intelligent Diagnostic Assistance.

BACKGROUND AND OBJECTIVES: We developed an automated morphological image recognition deep learning system (image recognition DLS) of peripheral blood cells, then constructed the diagnostic assist DLS combining image recognition DLS data with complete blood count (CBC) data. This study aimed to evaluate the clinical performance of the image recognition DLS and the diagnostic assist DLS in routine e...

Student Perceptions of a Custom Artificial Intelligence Clinical Case Companion.

INTRODUCTION: Artificial intelligence tools show promise in supplementing traditional physician assistant education, particularly in developing clinic...

Leveraging Machine Learning for Rapid and Accurate Diagnosis of Acute Leukemia.

CONTEXT: Early detection of acute leukemia (AL) is crucial for timely intervention and improved outcomes. Machine learning (ML) models provide a promi...

Effectiveness of artificial intelligence in classification of connective tissue diseases in patients with anti-nuclear antibody (ANA) positivity.

OBJECTIVES: The study aimed to investigate the classification performance of artificial intelligence (AI) in diagnosing connective tissue diseases(CTD...

Unsupervised machine learning to explore inflammation following cardiopulmonary bypass.

IntroductionCardiac surgery with cardiopulmonary bypass (CPB) often induces systemic inflammatory reaction syndrome (SIRS), affecting postoperative ou...

An interpretable machine learning approach to evaluate 30-day mortality risk in patients with community-onset bacteremia.

BACKGROUND: Machine learning (ML) techniques are increasingly being used in health outcome research to develop predictive models. However, ML models a...

Identification of key genes in intracranial aneurysm via WGCNA: insights into immune microenvironment and therapeutic targets.

BACKGROUND: Intracranial aneurysms (IA) are prevalent vascular lesions whose rupture causes subarachnoid hemorrhage with high disability and mortality...

Efficient lightweight CNN for automated classification of B-cell acute lymphoblastic leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is an aggressive hematological malignancy that primarily affects children but can also occur in adults, pr...

Machine Learning in Venous Thromboembolism-Why and What Next?

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE a...

Deep learning-based quantitative assessment of renal chronicity indices in lupus nephritis.

Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients. However, assessment by...

Hematopathology Practice in the Digital Era: What has Changed?

Hematopathology workflows are complex, since they include numerous data points necessary for guiding further testing, diagnosis, and patient managemen...

Development and validation of the APEX-HBD SCORe: a multivariable prediction model for postoperative complications in patients undergoing shoulder arthroplasty.

BACKGROUND: The incidence of total shoulder arthroplasty (TSA) has risen significantly, driven by expanded indications. This study aims to derive and ...

Chronic Ocular Sequelae of Stevens-Johnson Syndrome: Recent Advances in Understanding of Patho-Physiology and Management.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe mucocutaneous reactions primarily triggered by drugs or inf...

Graph Neural Networks for Realistic Bleeding Prediction in Surgical Simulators.

This study presents a novel approach using graph neural networks to predict the risk of internal bleeding using vessel maps derived from patient CT an...

Digital Pathology in Hematopathology: From Vision to Deployment.

Digital pathology (DP) has evolved alongside other technical advances, transforming our daily lives and diagnostic medicine. It is likely that, as in ...

Can medical students use artificial intelligence to learn transfusion? Evaluating ChatGPT responses to the American Society of Hematology medical student transfusion learning objectives.

BACKGROUND AND OBJECTIVES: Chat generative pretrained transformer (ChatGPT) is a large language model that is already in wide use among medical studen...

Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model.

Gastrointestinal bleeding (GIB) occurs more frequently in cardiovascular patients than in the general population, significantly affecting morbidity an...

SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.

BACKGROUND: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast ...

Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.

Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessm...