Hematology

Machine Learning-Based Prediction of Treatment Success, Postoperative Pain, and Analgesic Use After Full Pulpotomy in Permanent Molars with Symptomatic Irreversible Pulpitis.

INTRODUCTION: This study aimed to develop and evaluate machine learning (ML) models for predicting treatment success, postoperative pain, and analgesic use following full pulpotomy (FP) in permanent molars with symptomatic irreversible pulpitis (SIP). METHODS: This retrospective multicohort study included 214 molars with a minimum 2-year follow-up. ML models, including logistic regression (LR), su...

Evaluation of two large language models for intensive care unit discharge decisions: a prospective observational cohort study.

BACKGROUND: The aim of this study was to evaluate the effectiveness of two general-purpose Large Language Models (LLMs), ChatGPT and Gemini, in predicting Intensive Care Unit (ICU) discharge decisions (discharge vs. non-discharge). By comparing their outputs with decisions made by ICU physicians, we sought to determine the alignment of AI-generated recommendations with expert clinical judgment and...

Predictors of warfarin therapeutic dose in cancer patients: a preliminary comparative study using machine learning-integrated explainable artificial intelligence and Bayesian models.

BACKGROUND: Warfarin dosing in cancer patients is challenging due to altered pathophysiology and variable responses. While machine learning offers pre...

A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning.

Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumo...

Cyclosporine in hematological disorders: mechanisms, clinical practice and emerging advances.

Cyclosporine A (CsA) functions as a calcineurin inhibitor that perturbs T cell activation via calcineurin-nuclear factor of activated T cells (CaN-NFA...

Beyond prediction: explainable machine learning for ferritin estimation from complete blood count.

BACKGROUND: Serum ferritin is a primary marker of body iron stores; however, its diagnostic utility is limited by cost and non-specific elevation duri...

Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study.

BACKGROUND: Trauma is a leading cause of morbidity and mortality worldwide, particularly in younger populations. Early identification of high-risk tra...

Capturing multi-disease states on a spectrum with machine learning and routine clinical data.

BACKGROUND: Diseases exist on spectra of risk factors, cellular perturbations, organ dysfunction, and clinical manifestations. It is unknown whether t...

Predictors of warfarin therapeutic dose across thromboembolic indications: an exploratory early clinical evaluation study using machine learning-integrated explainable artificial intelligence and Bayesian models.

BACKGROUND: Warfarin dosing varies widely due to genetic, demographic, and clinical factors, but it is unknown whether the importance, equilibrium and...

Advances in Digital Pathology and Artificial Intelligence in the Diagnosis of Myeloid Neoplasms.

Recent advances in digital pathology and artificial intelligence (AI) are transforming our ability to diagnose myeloid neoplasms, including acute myel...

Traumatic Inflammatory-Coagulation Coactivation Occurs Early After Injury and Impacts Mortality.

OBJECTIVES: Acute traumatic coagulopathy is known to occur early following severe injury. However, the role and impact of both pro- and anti-inflammat...

Development and Validation of an Integrated Artificial Intelligence-Powered Clinical Decision Support Application for Cardiac Critical Care: Combining Coagulation Management, Blood Gas Analysis, Mechanical Ventilation, Hemodynamic Assessment, and Extracorporeal Membrane Oxygenation.

This study describes the development and preliminary validation of an integrated clinical decision support application (Cardiac Critical Care Advisor ...

A machine learning-based interpretable model for predicting pancreatic cancer in chronic pancreatitis patients with focal pancreatic lesions.

BACKGROUND: Pancreatic cancer (PC) is deadly and distinguishing it from inflammatory conditions in chronic pancreatitis (CP) patients is challenging. ...

Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.

OBJECTIVE: Postoperative biochemical recurrence (BCR) of prostate cancer (PCa) remains a major clinical challenge, and traditional risk assessment sys...

Machine-learning-model based tool for screening bone metastases from lung cancer patients in primary care practice.

PURPOSE: This study aimed to use the machine-learning methods to predict bone metastasis (BM) in patients with lung cancer. METHODS: This study includ...

Interpretable machine learning model using peripheral blood for non-invasive detection of moderate-to-severe myelofibrosis in JAK2 V617F-positive MPNs: A multicentre pilot proof-of-concept study.

Progression to moderate-to-severe myelofibrosis (MF) in JAK2 V617F-positive myeloproliferative neoplasms (MPNs) is often clinically silent, and bone m...

Colorless image processing technique combining chromaticity transfer and image feature extraction in visual graphic design.

This study presents a verifiable framework for high-fidelity colorless image processing by integrating depth-guided chrominance transfer with multi-le...

A machine learning-based risk prediction framework for atypical hyperplasia and endometrial cancer in postmenopausal women.

BACKGROUND: This study aimed to develop and internally validate a machine learning-based model for predicting endometrial malignancy, defined as atypi...

Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients.

BACKGROUND: Spontaneous bacterial peritonitis (SBP) remains a life-threatening complication of liver cirrhosis, requiring accurate and rapid predictio...

Machine learning to predict occult neck node metastasis in early oral squamous cell carcinoma - a comparison of models to risk stratify patients undergoing a sentinel lymph node biopsy.

BACKGROUND: Sentinel node biopsy (SNB) provides pathological staging of the neck in T1/T2 node-negative oral squamous cell carcinoma (OSCC). Up to 85%...