Cargando…

Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

This retrospective cohort study deals with evaluating severity of COVID-19 cases on the first symptoms and blood-test results of infected patients admitted to Emergency Department of Koc University Hospital (Istanbul, Turkey). To figure out remarkable hematological characteristics and risk factors i...

Descripción completa

Detalles Bibliográficos
Autores principales:	Kocadagli, Ozan, Baygul, Arzu, Gokmen, Neslihan, Incir, Said, Aktan, Cagdas
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Elsevier Masson SAS. 2022
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577545/ https://www.ncbi.nlm.nih.gov/pubmed/34768217 http://dx.doi.org/10.1016/j.retram.2021.103319

Ejemplares similares

COVID-19 studies involving machine learning methods: A bibliometric study
por: Baygül Eden, Arzu, et al.
Publicado: (2023)

Acrylamide in Corn-Based Thermally Processed Foods: A Review
por: Žilić, Slađana, et al.
Publicado: (2022)

Dietary exposure to acrylamide: A critical appraisal on the conversion of disregarded intermediates into acrylamide and possible reactions during digestion
por: Aktağ, Işıl Gürsul, et al.
Publicado: (2022)

An interpretable machine learning framework for diagnosis and prognosis of COVID-19
por: Fan, Yongxian, et al.
Publicado: (2023)

The Speed of Ingestion of a Sugary Beverage Has an Effect on the Acute Metabolic Response to Fructose
por: Kanbay, Mehmet, et al.
Publicado: (2021)

An interpretable and versatile machine learning approach for oocyte phenotyping
por: Letort, Gaelle, et al.
Publicado: (2022)

Application of interpretable machine learning for early prediction of prognosis in acute kidney injury
por: Hu, Chang, et al.
Publicado: (2022)

Validation of the Modified Berlin Questionnaire for the Diagnosis of Obstructive Sleep Apnea in Patients with a History of COVID-19 Infection
por: Celik, Yeliz, et al.
Publicado: (2023)

An interpretable machine learning approach to identify mechanism of action of antibiotics
por: Mongia, Mihir, et al.
Publicado: (2022)

Interpretable Machine Learning with Python
por: Masis, Serg
Publicado: (2021)

Interpretable machine learning for genomics
por: Watson, David S.
Publicado: (2021)

Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study
por: Hu, Chang, et al.
Publicado: (2022)

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning
por: Tritt, Andrew, et al.
Publicado: (2023)

"What is relevant in a text document?": An interpretable machine learning approach
por: Arras, Leila, et al.
Publicado: (2017)

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach
por: Kolyshkina, Inna, et al.
Publicado: (2021)

Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach
por: Prosperi, Mattia C F, et al.
Publicado: (2014)

Editorial: Human-Interpretable Machine Learning
por: Tolomei, Gabriele, et al.
Publicado: (2022)

A framework for vehicle quality evaluation based on interpretable machine learning
por: Alwadi, Mohammad, et al.
Publicado: (2022)

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling
por: Luo, Yi, et al.
Publicado: (2019)

A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation
por: Inbar, Or, et al.
Publicado: (2021)

A Machine Learning Approach for Recommending Herbal Formulae with Enhanced Interpretability and Applicability
por: Lee, Won-Yung, et al.
Publicado: (2022)

Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture
por: Štajduhar, Andrija, et al.
Publicado: (2023)

Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
por: Deppner, Juergen, et al.
Publicado: (2023)

Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach
por: Wu, Yuanyuan, et al.
Publicado: (2023)

An interpretable machine learning approach to study the relationship beetwen retrognathia and skull anatomy
por: Makaremi, Masrour, et al.
Publicado: (2023)

R.ROSETTA: an interpretable machine learning framework
por: Garbulowski, Mateusz, et al.
Publicado: (2021)

Interpretable Machine Learning of Two‐Photon Absorption
por: Su, Yuming, et al.
Publicado: (2023)

Advancing Computational Toxicology by Interpretable Machine Learning
por: Jia, Xuelian, et al.
Publicado: (2023)

Clinical Evaluation of Decompressive Craniectomy in Malignant Middle Cerebral Artery Infarction using 3D Area and Volume Calculations
por: Kitiş, Serkan, et al.
Publicado: (2021)

Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach
por: Dobrojevic, Milos, et al.
Publicado: (2023)

Evaluating molecular representations in machine learning models for drug response prediction and interpretability
por: Baptista, Delora, et al.
Publicado: (2022)

Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning
por: Akcay, Melek, et al.
Publicado: (2020)

Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach
por: Braghetto, Anna, et al.
Publicado: (2023)

Hybrid machine learning approach for landslide prediction, Uttarakhand, India
por: Kainthura, Poonam, et al.
Publicado: (2022)

A systematic hybrid machine learning approach for stress prediction
por: Ding, Cheng, et al.
Publicado: (2023)

Breast Cancer Prognosis Using a Machine Learning Approach
por: Ferroni, Patrizia, et al.
Publicado: (2019)

Disruption evaluation in end-to-end semiconductor supply chains via interpretable machine learning
por: Jaenichen, Friedrich-Maximilian, et al.
Publicado: (2022)

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool
por: Celecia, Alimed, et al.
Publicado: (2021)

Explainable and interpretable models in computer vision and machine learning
por: Escalante, Hugo Jair, et al.
Publicado: (2019)

An interpretable machine learning model for diagnosis of Alzheimer's disease
por: Das, Diptesh, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_p859n6nknmc1d487f1k6aq9hhn