Cargando…

Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD. Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hoodbhoy, Zahra, Jiwani, Uswa, Sattar, Saima, Salam, Rehana, Hasan, Babar, Das, Jai K.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2021
Materias:	Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297386/ https://www.ncbi.nlm.nih.gov/pubmed/34308341 http://dx.doi.org/10.3389/frai.2021.708365

Ejemplares similares

Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis
por: Parkash, Om, et al.
Publicado: (2022)

Classification model for accuracy and intrusion detection using machine learning approach
por: Agarwal, Arushi, et al.
Publicado: (2021)

A Novel Bayesian General Medical Diagnostic Assistant Achieves Superior Accuracy With Sparse History: A Performance Comparison of 7 Online Diagnostic Aids and Physicians
por: Jones, Alicia M., et al.
Publicado: (2022)

Machine learning approaches to identify Parkinson's disease using voice signal features
por: Alshammri, Raya, et al.
Publicado: (2023)

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water
por: Weller, Daniel L., et al.
Publicado: (2021)

Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning
por: Salem, Soumaya Ben, et al.
Publicado: (2023)

The Moral Choice Machine
por: Schramowski, Patrick, et al.
Publicado: (2020)

Achieving descriptive accuracy in explanations via argumentation: The case of probabilistic classifiers
por: Albini, Emanuele, et al.
Publicado: (2023)

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays
por: Maharjan, Jenish, et al.
Publicado: (2021)

Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain
por: Loued-Khenissi, Leyla, et al.
Publicado: (2020)

Improving word embeddings in Portuguese: increasing accuracy while reducing the size of the corpus
por: Pinto, José Pedro, et al.
Publicado: (2022)

Editorial: Machine Learning for Water Resources
por: Mariethoz, Gregoire, et al.
Publicado: (2021)

Editorial: Theory of Mind in Humans and in Machines
por: Langley, Christelle, et al.
Publicado: (2022)

Machine translation and foreign language education
por: Urlaub, Per, et al.
Publicado: (2022)

Opportunities for human factors in machine learning
por: Baweja, Jessica A., et al.
Publicado: (2023)

GDP Forecasting: Machine Learning, Linear or Autoregression?
por: Maccarrone, Giovanni, et al.
Publicado: (2021)

A Survey of Topological Machine Learning Methods
por: Hensel, Felix, et al.
Publicado: (2021)

Backpropagation-Based Decoding for Multimodal Machine Translation
por: Yang, Ziyan, et al.
Publicado: (2022)

Integration of AI and Machine Learning in Radiotherapy QA
por: Chan, Maria F., et al.
Publicado: (2020)

Leveraging explanations in interactive machine learning: An overview
por: Teso, Stefano, et al.
Publicado: (2023)

Machine learning and deep learning approaches in IoT
por: Javed, Abqa, et al.
Publicado: (2023)

Using machine learning for healthcare treatment planning
por: Dubey, Snigdha, et al.
Publicado: (2023)

Predicting ward transfer mortality with machine learning
por: Lezama, Jose L., et al.
Publicado: (2023)

Corrigendum: Opportunities for human factors in machine learning
por: Baweja, Jessica A., et al.
Publicado: (2023)

Identification of Scams in Initial Coin Offerings With Machine Learning
por: Karimov, Bedil, et al.
Publicado: (2021)

Impact of Box-Cox Transformation on Machine-Learning Algorithms
por: Blum, Luca, et al.
Publicado: (2022)

Transfer Across Different Machines by Transfer Function Estimation
por: Matania, Omri, et al.
Publicado: (2022)

Applied Machine Learning for Stochastic Local Volatility Calibration
por: Hakala, Jürgen
Publicado: (2019)

Topology Applied to Machine Learning: From Global to Local
por: Adams, Henry, et al.
Publicado: (2021)

Roof type classification with innovative machine learning approaches
por: Ölçer, Naim, et al.
Publicado: (2023)

Supervised machine learning models for depression sentiment analysis
por: Obagbuwa, Ibidun Christiana, et al.
Publicado: (2023)

Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain
por: Tabares-Soto, Reinel, et al.
Publicado: (2021)

Predictions of bitcoin prices through machine learning based frameworks
por: Cocco, Luisanna, et al.
Publicado: (2021)

Generalizable Machine Learning in Neuroscience Using Graph Neural Networks
por: Wang, Paul Y., et al.
Publicado: (2021)

Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces
por: Hernández-Orozco, Santiago, et al.
Publicado: (2021)

Survey on graph embeddings and their applications to machine learning problems on graphs
por: Makarov, Ilya, et al.
Publicado: (2021)

Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
por: Luo, Wei
Publicado: (2021)

A United States Fair Lending Perspective on Machine Learning
por: Hall, Patrick, et al.
Publicado: (2021)

A Comparative Study of Machine Learning Methods for Persistence Diagrams
por: Barnes, Danielle, et al.
Publicado: (2021)

Understanding Musical Predictions With an Embodied Interface for Musical Machine Learning
por: Martin, Charles Patrick, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_68l0hm2hhv0um1putkvpn2o8jv