Assessment of Machine Learning–Based Medical Directives to Expedite Care in Pediatric Emergency Medicine
Sign Up to like & getrecommendations! Published in 2022 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.2599
Abstract: Key Points Question Can machine learning–based medical directives (MLMDs) be used to autonomously order testing at triage for common pediatric presentations in the emergency department? Findings This decision analytical model analyzing 77 219 presentations of children… read more here.
Keywords: emergency; machine learning; medical directives; based medical ... See more keywords
Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs
Sign Up to like & getrecommendations! Published in 2023 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.53820
Abstract: Key Points Question Can a deep learning–based synthetic bone-suppressed (DLBS) model additionally improve the detection of pulmonary nodules on chest radiographs? Findings In this decision analytical modeling study of 1449 patients, the DLBS model was… read more here.
Keywords: detection; chest radiographs; deep learning; model ... See more keywords
A sensitivity analysis of probability maps in deep‐learning‐based anatomical segmentation
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13331
Abstract: Abstract Purpose Deep‐learning‐based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep‐learning applications such as… read more here.
Keywords: segmentation; anatomical segmentation; deep learning; learning based ... See more keywords
Dosimetric assessment of patient dose calculation on a deep learning‐based synthesized computed tomography image for adaptive radiotherapy
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13595
Abstract: Abstract Purpose Dose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed… read more here.
Keywords: computed tomography; deep learning; learning based; image ... See more keywords
Evaluating the dosimetric impact of deep‐learning‐based auto‐segmentation in prostate cancer radiotherapy: Insights into real‐world clinical implementation and inter‐observer variability
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.14569
Abstract: Abstract Purpose This study aimed to investigate the dosimetric impact of deep‐learning‐based auto‐contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geometric accuracy… read more here.
Keywords: deep learning; cancer radiotherapy; auto; prostate cancer ... See more keywords
Evaluation and failure analysis of four commercial deep learning‐based autosegmentation software for abdominal organs at risk
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.70010
Abstract: Abstract Purpose Deep learning‐based segmentation of organs‐at‐risk (OAR) is emerging to become mainstream in clinical practice because of the superior performance over atlas and model‐based autocontouring methods. While several commercial deep learning‐based autosegmentation solutions are… read more here.
Keywords: deep learning; segmentation; autosegmentation; learning based ... See more keywords
Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites
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DOI: 10.1002/adem.201901197
Abstract: The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a… read more here.
Keywords: microstructural characterization; advanced deep; deep learning; matrix composites ... See more keywords
Bridging the gap: Development of an experiential learning–based health disparities curriculum
Sign Up to like & getrecommendations! Published in 2022 at "AEM Education and Training"
DOI: 10.1002/aet2.10820
Abstract: The increasing number of vulnerable populations served by the emergency department (ED) calls for developing and implementing curricula aimed at training residents to deliver quality care for the most marginalized groups. read more here.
Keywords: experiential learning; development experiential; based health; bridging gap ... See more keywords
Development and evaluation of a colorectal cancer screening method using machine learning‐based gut microbiota analysis
Sign Up to like & getrecommendations! Published in 2022 at "Cancer Medicine"
DOI: 10.1002/cam4.4671
Abstract: Accumulating evidence indicates that alterations of gut microbiota are associated with colorectal cancer (CRC). Therefore, the use of gut microbiota for the diagnosis of CRC has received attention. Recently, several studies have been conducted to… read more here.
Keywords: learning based; machine learning; cancer; gut microbiota ... See more keywords
Expected and observed in‐hospital mortality in heart failure patients before and during the COVID‐19 pandemic: Introduction of the machine learning‐based standardized mortality ratio at Helios hospitals
Sign Up to like & getrecommendations! Published in 2021 at "Clinical Cardiology"
DOI: 10.1002/clc.23762
Abstract: Reduced hospital admission rates for heart failure (HF) and evidence of increased in‐hospital mortality were reported during the COVID‐19 pandemic. The aim of this study was to apply a machine learning (ML)‐based mortality prediction model… read more here.
Keywords: learning based; machine learning; heart failure; mortality ... See more keywords
Forecasting Crude Oil Volatility Using the Deep Learning‐Based Hybrid Models With Common Factors
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Futures Markets"
DOI: 10.1002/fut.22529
Abstract: Based on empirical evidence of the Chinese commodity futures volatility dynamics, we propose a novel and flexible hybrid model, denoted as SAE‐HAR‐DL, which combines a supervised autoencoder (AE) with the deep learning‐based HAR model framework… read more here.
Keywords: deep learning; common factors; volatility; crude oil ... See more keywords