Latest AI and machine learning research in copd for healthcare professionals.
Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD) for prospective clinical trial screening and buprenorphine initiation. We conducted a multi-phase study across three EDs in a single United States health system from 2014 to 2025. Using visit-level data available at or before triage, we trained a r...
Segmenting sleep into fixed 30-second epochs remains central to current sleep scoring practice, yet it imposes rigid boundaries that may not accurately reflect the true temporal sleep dynamics. We aimed to develop a deep learning-based, high-temporal-resolution sleep-wake classifier leveraging temporally continuous manual reference scoring without fixed epoch boundaries and transfer learning techn...
BACKGROUND: This research aims to explore the possible link between Vitamin C Intake (VCI) and the incidence of Chronic Obstructive Pulmonary Disease ...
BACKGROUND: To evaluate the feasibility and practicality of home spirometry telemonitoring for pediatric patients with asthma, including both motivato...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major contributor to global morbidity and mortality, particularly during acute exacerbat...
OBJECTIVE: The purpose of this study was to develop and validate machine learning models that can predict superaverage length of stay in hypercapnic-t...
BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanis...
PURPOSE: The aim is to develop a learning model based on clinical and survey data to assess sleep quality and identify determining factors affecting s...
Accurate anatomical labeling and analysis of the pulmonary structure and its surrounding anatomy from thoracic CT is getting increasingly important ...
Pulmonary Embolisms (PE) are a leading cause of cardiovascular death. Computed tomographic pulmonary angiography (CTPA) stands as the gold standard ...
Score-based Generative Models (SGMs) have demonstrated remarkable generalization abilities, e.g. generating unseen, but natural data. However, the g...
Training data attribution (TDA) is the task of attributing model behavior to elements in the training data. This paper draws attention to the common...
This paper compares the accuracy of the terms extracted using SketchEngine, TBXTools and ChatGPT. In addition, it evaluates the quality of the defin...
Most approaches to machine learning evaluation assume that machine and human responses are repeatable enough to be measured against data with unitar...
Purpose To measure the benefit of single-phase CT, inspiratory-expiratory CT, and clinical data for convolutional neural network (CNN)-based chronic o...
While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less...
The growing volume of biomedical scholarly document abstracts presents an increasing challenge in efficiently retrieving accurate and relevant infor...
Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even...
This research utilizes a deep learning-based image generation algorithm to generate pseudo-sagittal STIR sequences from sagittal T1WI and T2WI MR imag...
Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chr...