Comparison of nomogram and machine‐learning methods for predicting the survival of non‐small cell lung cancer patients-Reference-Cited by-同舟云学术

Comparison of nomogram and machine‐learning methods for predicting the survival of non‐small cell lung cancer patients

Published:2022-08 Issue:2 Volume:1 Page:135-145
ISSN:2770-9183
Container-title:Cancer Innovation
language:en
Short-container-title:Cancer Innovation

Author:

Lei Haike¹,Li Xiaosheng¹,Ma Wuren²,Hong Na²,Liu Chun²,Zhou Wei¹,Zhou Hong¹,Gong Mengchun²,Wang Ying¹,Wang Guixue³,Wu Yongzhong¹

Affiliation:

1. Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer Hospital Chongqing China

2. Digital Health China Technologies, Co., Ltd. Beijing China

3. MOE Key Lab for Biorheological Science and Technology, State and Local Joint Engineering Laboratory for Vascular Implants College of Bioengineering Chongqing University Chongqing China

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cai2.24

Reference35 articles.

1. A System for Classifying Disease Comorbidity Status from Medical Discharge Summaries Using Automated Hotspot and Negated Concept Detection

2. Development of a Prognostic Nomogram for Patients with Lung Adenocarcinoma in the Stages I, II, and III Based on Immune Scores

3. NCCN Guidelines Insights: Non–Small Cell Lung Cancer, Version 4.2016

4. Building a Nomogram for Survey-Weighted Cox Models UsingR

5. Nomograms in oncology: more than meets the eye

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