Cargando…

Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care

BACKGROUND: This study used machine learning to develop a 3-year lung cancer risk prediction model with large real-world data in a mostly younger population. METHODS: Over 4.7 million individuals, aged 45 to 65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment pr...

Descripción completa

Detalles Bibliográficos
Autores principales:	Chandran, Urmila, Reps, Jenna, Yang, Robert, Vachani, Anil, Maldonado, Fabien, Kalsekar, Iftekhar
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	American Association for Cancer Research 2023
Materias:	Research Articles
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986687/ https://www.ncbi.nlm.nih.gov/pubmed/36576991 http://dx.doi.org/10.1158/1055-9965.EPI-22-0873

Ejemplares similares

Improving visual communication of discriminative accuracy for predictive models: the probability threshold plot
por: Johnston, Stephen S, et al.
Publicado: (2021)

Can machine-learning improve cardiovascular risk prediction using routine clinical data?
por: Weng, Stephen F., et al.
Publicado: (2017)

Patient Preferences for Lung Cancer Interception Therapy
por: Janssen, Ellen M., et al.
Publicado: (2023)

Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases
por: Chandran, Urmila, et al.
Publicado: (2019)

Real-world glycemic outcomes in patients with type 2 diabetes initiating exenatide once weekly and liraglutide once daily: a retrospective cohort study
por: Saunders, William B, et al.
Publicado: (2016)

Finding treatment‐resistant depression in real‐world data: How a data‐driven approach compares with expert‐based heuristics
por: Cepeda, M. Soledad, et al.
Publicado: (2017)

Real-World Data and Machine Learning to Predict Cardiac Amyloidosis
por: García-García, Elena, et al.
Publicado: (2021)

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations
por: Wong, Jenna, et al.
Publicado: (2022)

Diagnostic outcomes of robotic-assisted bronchoscopy for pulmonary lesions in a real-world multicenter community setting
por: Khan, Faisal, et al.
Publicado: (2023)

Real-world machine learning
por: Brink, Henrik, et al.
Publicado: (2017)

Real-world outcomes comparison among adults with atrial fibrillation undergoing catheter ablation with a contact force porous tip catheter versus a second-generation cryoballoon catheter: a retrospective analysis of multihospital US database
por: Costea, Alexandru, et al.
Publicado: (2020)

Comparison of real-world clinical and economic outcomes between the ThermoCool(®) SF and ThermoCool(®) catheters in patients undergoing radiofrequency catheter ablation for atrial fibrillation
por: Chinitz, Larry, et al.
Publicado: (2018)

Real-world characteristics and readmissions among patients undergoing ablation for ventricular tachycardia: a retrospective database analysis of commercially insured patients in the USA
por: Mehta, Vinay, et al.
Publicado: (2020)

Machine learning logistics: model management in the real world
por: Dunning, Ted, et al.
Publicado: (2017)

Application of machine learning in predicting survival outcomes involving real-world data: a scoping review
por: Huang, Yinan, et al.
Publicado: (2023)

Immune Checkpoint Inhibitor Uptake in Real-World Patients With Malignant Pleural Mesothelioma
por: Kim, Roger Y., et al.
Publicado: (2021)

Neonatal mortality prediction with routinely collected data: a machine learning approach
por: Batista, André F. M., et al.
Publicado: (2021)

Uptake of novel systemic therapy: Real world patterns among adults with advanced non-small cell lung cancer
por: Carroll, Nikki M., et al.
Publicado: (2023)

An application of machine learning based on real-world data: Mining features of fibrinogen in clinical stages of lung cancer between sexes
por: Yin, Fangtao, et al.
Publicado: (2021)

Scala machine learning projects: build real-world machine learning and deep learning projects with Scala
por: Karim, Md Rezaul
Publicado: (2018)

Python: real world machine learning : learn to solve challenging data science problems by building powerful machine learning models using Python
por: Joshi, Prateek
Publicado: (2016)

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data
por: Mar, Javier, et al.
Publicado: (2020)

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm
por: Chen, Yu-Ming, et al.
Publicado: (2023)

Predicting the Real-World Future of Glaucoma Patients? Cautions Are Required for Machine Learning
por: Long, Erping, et al.
Publicado: (2017)

Approach to machine learning for extraction of real-world data variables from electronic health records
por: Adamson, Blythe, et al.
Publicado: (2023)

Machine Learning: Algorithms, Real-World Applications and Research Directions
por: Sarker, Iqbal H.
Publicado: (2021)

Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach
por: Wolff, J., et al.
Publicado: (2020)

Real world active learning: applications and strategies for human-in-the-loop machine learning
por: Cuzzillo, Ted
Publicado: (2015)

Practical machine learning with R: define, build, and evaluate machine learning models for real-world applications
por: Jeyaraman, Brindha Priyadarshini, et al.
Publicado: (2019)

Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data
por: Guo, Kai, et al.
Publicado: (2021)

Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data
por: Pivneva, Irina, et al.
Publicado: (2022)

Could DNA Fragments Be the Key to Early Detection of Lung Cancer?
por: Thompson, Jeffrey C., et al.
Publicado: (2023)

Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data
por: Reps, Jenna M., et al.
Publicado: (2019)

A Novel Prognostic Scoring System of Intrahepatic Cholangiocarcinoma With Machine Learning Basing on Real-World Data
por: Li, Zhizhen, et al.
Publicado: (2021)

Machine learning in a real-world PFO study: analysis of data from multi-centers in China
por: Luo, Dongling, et al.
Publicado: (2022)

Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning
por: Benedum, Corey M., et al.
Publicado: (2023)

Safety data in randomised real-world evidence studies: Salford Lung Study learnings
por: Harvey, Catherine, et al.
Publicado: (2021)

The impact of an immunization training certificate program on the perceived knowledge, skills and attitudes of pharmacy students toward pharmacy-based immunizations
por: Marcum, Zachary A., et al.
Publicado: (2010)

Comparison of charges and resource use associated with saxagliptin and sitagliptin
por: Vaidya, Varun, et al.
Publicado: (2016)

Brazilian disaster datasets and real-world instances for optimization and machine learning
por: Veloso, Rafaela, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_979b2n45o88dmrc37hscgroi99