Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality-Reference-Cited by-同舟云学术

Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality

Published:2020-11-03 Issue:11 Volume:3 Page:e2023780
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Kanwal Fasiha¹²³⁴,Taylor Thomas J.⁵,Kramer Jennifer R.²³⁴,Cao Yumei³⁴,Smith Donna³⁴,Gifford Allen L.⁶⁷,El-Serag Hashem B.¹²³⁴,Naik Aanand D.²³⁴⁸,Asch Steven M.³⁹

Affiliation:

1. Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas

2. Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas

3. Veterans Affairs (VA) Health Services Research and Development Service Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas

4. Michael E. DeBakey VA Medical Center, Houston, Texas

5. Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California

6. Department of Medicine, VA Boston Healthcare System, Boston University, Boston, Massachusetts

7. Department of Health Law, Policy, and Management, VA Boston Healthcare System, Boston University, Boston, Massachusetts

8. Section of Geriatrics and Palliative Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas

9. Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, California

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2772522/kanwal_2020_oi_200788_1603750105.73282.pdf

Reference42 articles.

1. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the Euro Heart Survey on Atrial Fibrillation.;Lip;Chest,2010

2. Evaluation of risk stratification schemes for ischaemic stroke and bleeding in 182 678 patients with atrial fibrillation: the Swedish Atrial Fibrillation cohort study.;Friberg;Eur Heart J,2012

3. Validation of severity scoring systems SAPS II and APACHE II in a single-center population.;Capuzzo;Intensive Care Med,2000

4. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.;Rudin;Nature Machine Intelligence.,2019

5. A model to predict survival in patients with end-stage liver disease.;Kamath;Hepatology,2001

Cited by 57 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AI-Enabled Clinical Decision Support System Modeling for the Prediction of Cirrhosis Complications;IRBM;2024-10

2. PNPLA3, Obesity, and Heavy Alcohol Use in Cirrhosis Patients May Exert a Synergistic Increase Hepatocellular Carcinoma Risk;Clinical Gastroenterology and Hepatology;2024-09

3. A Liver Cirrhosis Segmentation and Detection Using Modified Deep Learning Model;International Journal of Scientific Research in Science and Technology;2024-08-13

4. Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis;Antimicrobial Resistance & Infection Control;2024-08-07

5. Development and validation of a predictive model for prolonged length of stay in elderly type 2 diabetes mellitus patients combined with cerebral infarction;Frontiers in Neurology;2024-08-01