Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

Journal: Asian Pacific journal of cancer prevention : APJCP

Published Date: Jan 1, 2015

Abstract

BACKGROUND: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent.

Authors

Jian Chen

School of Pharmacy, Shanghai Jiaotong University, Shanghai, China.
Jie Chen

School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
Hong-Yan Ding
Qin-Shi Pan
Wan-Dong Hong
Gang Xu

University Hospitals of Leicester NHS Trust; UK.
Fang-You Yu
Yu-Min Wang

Keywords

Adenocarcinoma Adult Aged Aged, 80 and over Carcinoma, Large Cell Carcinoma, Non-Small-Cell Lung Carcinoma, Squamous Cell Cross Infection Female Follow-Up Studies Fungi Humans Incidence Logistic Models Lung Neoplasms Male Middle Aged Mycoses Neoplasm Staging Neural Networks, Computer Prognosis Retrospective Studies Risk Factors ROC Curve Small Cell Lung Carcinoma

External Resources

View on PubMed Access via DOI PubMed (26163648)

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

Abstract

Authors

Keywords

External Resources

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