Purpose: Patients with moderate-to-severe chronic obstructive pulmonary disease (COPD) face a high risk of three-month unplanned readmission after discharge, yet early identification of high-risk patients remains challenging in clinical practice. This study aimed to develop and validate machine learning (ML) models for predicting the risk and providing a reference for the subsequent development and updating… Continue reading Development and Validation of a Machine Learning Model for Predicting Three-Month Unplanned Readmission in Patients with Moderate-to-Severe Chronic Obstructive Pulmonary Disease: A Retrospective Cohort Study
Category: Tech
Artificial intelligence in myeloproliferative neoplasms: From digital bone marrow pathology to clinical decision support
Myeloproliferative neoplasms (MPN) are clonal hematopoietic stem cell disorders whose diagnosis, prognostication, and management depend on integrating heterogeneous clinical, morphological, and molecular data. This narrative review (PubMed/MEDLINE searched through August 2026, supplemented by hand-searching and conference abstracts) appraises the evidence for AI applications across the MPN continuum. Artificial intelligence (AI), and its principal subfields, machine… Continue reading Artificial intelligence in myeloproliferative neoplasms: From digital bone marrow pathology to clinical decision support
Multi-method Machine-Learning Identification of IL11 as a Key Gene in Kidney Stone Disease: Mechanistic Clues and Diagnostic Potential
Background: Randall's plaque (RP) is a key initiating lesion of idiopathic calcium stones at the renal papilla, and transcriptomic profiling of human RP has revealed inflammation- and matrix-remodeling-related changes, including upregulation of interleukin 11 (IL11). However, under a pure kidney-stone phenotype, a rigorous framework combining multi-algorithm consensus screening with fuller diagnostic appraisal (calibration, internal validation,… Continue reading Multi-method Machine-Learning Identification of IL11 as a Key Gene in Kidney Stone Disease: Mechanistic Clues and Diagnostic Potential
User Beware: Inaccuracy and Inconsistency of Large Language Models in Providing Precision Dosing Recommendations for Patients With Kidney Impairment-A Case Series
Introduction: Evidence-based dosing guidance for medications in critically ill patients with acute kidney injury (AKI) and receiving continuous kidney replacement therapy (CKRT) is limited. Freely available large language models (LLMs) can generate confident, human-like outputs. The accuracy and reproducibility of LLMs in providing precision drug dosing recommendations in the setting of AKI and CKRT have… Continue reading User Beware: Inaccuracy and Inconsistency of Large Language Models in Providing Precision Dosing Recommendations for Patients With Kidney Impairment-A Case Series
Development and Validation of an Interpretable Machine Learning-Based Prediction Model of Sepsis: A Retrospective Multicenter Cohort Study
Aims/background: Sepsis is a leading cause of morbidity and mortality in intensive care units (ICUs). Early identification of patients at high risk of death is essential to guide timely interventions and optimize resource allocation. This study aimed to develop and validate an interpretable machine learning model for predicting 28-day mortality in adult ICU patients.Methods: We… Continue reading Development and Validation of an Interpretable Machine Learning-Based Prediction Model of Sepsis: A Retrospective Multicenter Cohort Study
Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM
Background: Occult peritoneal metastasis (OPM) in gastric cancer is frequently missed on preoperative contrast-enhanced computed tomography (CT), leading to understaging in about 30-40% of patients and necessitating invasive laparoscopy for confirmation. A non-invasive tool to preoperatively identify OPM would help avoid unnecessary surgery and guide individualized treatment, but existing models lack interpretability and do not… Continue reading Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM
Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study
Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use. This study therefore proposes a highly sensitive artificial intelligence (AI)-assisted diagnostic… Continue reading Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study
Explainable Machine Learning for Predicting Deep Vein Thrombosis in Critically Ill Patients with COPD: Development and Multicenter External Validation
Background: Deep vein thrombosis (DVT) is a frequent yet underrecognized complication in critically ill patients with chronic obstructive pulmonary disease (COPD). Existing risk assessment tools are not specifically tailored to this high-risk population. We aimed to develop and externally validate an interpretable machine-learning model for early prediction of DVT in ICU-admitted COPD patients.Methods: Adult COPD… Continue reading Explainable Machine Learning for Predicting Deep Vein Thrombosis in Critically Ill Patients with COPD: Development and Multicenter External Validation
Digital Therapeutics for Postoperative Rehabilitation after Arthroscopic Rotator Cuff Repair: A Single-Center, Assessor-Blinded, Randomized Pilot Study
Background: Home-based rehabilitation after arthroscopic rotator cuff repair (ARCR) often lacks objective monitoring. To address this limitation, we developed the ANAPA digital rehabilitation (DR) system, which includes a patient-facing mobile application for clinical efficacy evaluation (ANAPA ME) and a physician-facing measurement application for technical assessment (ANAPA PS). This study compared the safety and preliminary efficacy… Continue reading Digital Therapeutics for Postoperative Rehabilitation after Arthroscopic Rotator Cuff Repair: A Single-Center, Assessor-Blinded, Randomized Pilot Study
