Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling.
| Autor: | Wang, Kung-Jeng, Chen, Jyun-Lin, Chen, Kun-Huang, Wang, Kung-Min |
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| Předmět: |
TREATMENT of lung tumors
AGE distribution CANCER chemotherapy CANCER patient psychology CANCER treatment CHRONIC diseases COMPARATIVE studies DATABASES ECONOMIC aspects of diseases ECOLOGY HELP-seeking behavior LENGTH of stay in hospitals INFORMATION storage & retrieval systems MEDICAL databases LONGITUDINAL method LUNG tumors MEDICAL care use MEDICAL care costs MEDICAL records POPULATION geography PROBABILITY theory RESEARCH funding RISK assessment SEX distribution SURVIVAL analysis (Biometry) TIME TUMOR classification COMORBIDITY RESIDENTIAL patterns SPECIALTY hospitals RETROSPECTIVE studies SEVERITY of illness index STATISTICAL models DESCRIPTIVE statistics ACQUISITION of data methodology ODDS ratio DISEASE risk factors |
| Zdroj: | Journal of Medical Systems; Mar2020, Vol. 44 Issue 3, p1-11, 11p, 2 Diagrams, 7 Charts, 2 Graphs |
| Abstrakt: | Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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