Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA
Abstract Machine learning (ML) has demonstrated promise in predicting mortality; however,
understanding spatial variation in risk factor contributions to mortality rate requires …
understanding spatial variation in risk factor contributions to mortality rate requires …
Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007
Lung cancer is the second most commonly diagnosed cancer in both men and women in
Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not …
Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not …
Interpretable machine learning prediction of all-cause mortality
Background Unlike linear models which are traditionally used to study all-cause mortality,
complex machine learning models can capture non-linear interrelations and provide …
complex machine learning models can capture non-linear interrelations and provide …
Machine-learning model to predict the cause of death using a stacking ensemble method for observational data
Objective Cause of death is used as an important outcome of clinical research; however,
access to cause-of-death data is limited. This study aimed to develop and validate a …
access to cause-of-death data is limited. This study aimed to develop and validate a …
Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinoma
T Zengin, T Önal-Süzek - Journal of personalized medicine, 2021 - mdpi.com
Lung cancer is the second most frequently diagnosed cancer type and responsible for the
highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung …
highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung …
CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data
MJ McGeachie, HH Chang… - PLoS computational …, 2014 - journals.plos.org
Bayesian Networks (BN) have been a popular predictive modeling formalism in
bioinformatics, but their application in modern genomics has been slowed by an inability to …
bioinformatics, but their application in modern genomics has been slowed by an inability to …
Assessing lung cancer absolute risk trajectory based on a polygenic risk model
Lung cancer is the leading cause of cancer-related death globally. An improved risk
stratification strategy can increase efficiency of low-dose CT (LDCT) screening. Here we …
stratification strategy can increase efficiency of low-dose CT (LDCT) screening. Here we …
Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and …
Objective Although lower respiratory infections (LRI) are among the leading causes of
mortality in the US, their association with underlying factors and geographic variation have …
mortality in the US, their association with underlying factors and geographic variation have …
Application of machine learning in predicting survival outcomes involving real-world data: a scoping review
Background Despite the interest in machine learning (ML) algorithms for analyzing real-
world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common …
world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common …
A deep learning-based framework for lung cancer survival analysis with biomarker interpretation
Background Lung cancer is the leading cause of cancer-related deaths in both men and
women in the United States, and it has a much lower five-year survival rate than many other …
women in the United States, and it has a much lower five-year survival rate than many other …