Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer-Reference-Cited by-同舟云学术

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer

Published:2021-07-07 Issue:7 Volume:4 Page:e2114723
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Yuan Qianyu¹,Cai Tianrun²,Hong Chuan³⁴,Du Mulong¹⁵,Johnson Bruce E.⁶⁷,Lanuti Michael⁸,Cai Tianxi³⁴,Christiani David C.¹⁹

Affiliation:

1. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

2. Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, Boston, Massachusetts

3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

4. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts

5. Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China

6. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts

7. Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, Massachusetts

8. Center for Thoracic Cancers, Division of Thoracic Surgery, Massachusetts General Hospital Cancer Center, Boston, Massachusetts

9. Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2781685/yuan_2021_oi_210446_1624650074.34279.pdf

Reference22 articles.

1. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.;Bray;CA Cancer J Clin,2018

2. An individual patient data metaanalysis of outcomes and prognostic factors after treatment of oligometastatic non-small-cell lung cancer.;Ashworth;Clin Lung Cancer,2014

3. Prognostic factors in non-small cell lung cancer: a decade of progress.;Brundage;Chest,2002

4. Small-cell lung cancer: prognostic factors and changing treatment over 15 years.;Gaspar;Clin Lung Cancer,2012

5. Performance status and smoking status are independent favorable prognostic factors for survival in non-small cell lung cancer: a comprehensive analysis of 26,957 patients with NSCLC.;Kawaguchi;J Thorac Oncol,2010

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