A survey of Bayesian Network structure learning
Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges
Abstract Fault Diagnosis and Health Monitoring (FD-HM) for modern control systems have
been an active area of research over the last few years. Model-based FD-HM computational …
been an active area of research over the last few years. Model-based FD-HM computational …
Application of artificial intelligence in lung cancer
Simple Summary Lung cancer is the leading cause of malignancy-related mortality
worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and …
worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and …
[HTML][HTML] Bayesian networks in healthcare: Distribution by medical condition
Bayesian networks (BNs) have received increasing research attention that is not matched by
adoption in practice and yet have potential to significantly benefit healthcare. Hitherto …
adoption in practice and yet have potential to significantly benefit healthcare. Hitherto …
[HTML][HTML] Distributed learning: develo** a predictive model based on data from multiple hospitals without data leaving the hospital–a real life proof of concept
Purpose One of the major hurdles in enabling personalized medicine is obtaining sufficient
patient data to feed into predictive models. Combining data originating from multiple …
patient data to feed into predictive models. Combining data originating from multiple …
[HTML][HTML] Bayesian networks for risk prediction using real-world data: a tool for precision medicine
Objective The fields of medicine and public health are undergoing a data revolution. An
increasing availability of data has brought about a growing interest in machine-learning …
increasing availability of data has brought about a growing interest in machine-learning …
A review on data‐driven learning approaches for fault detection and diagnosis in chemical processes
Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …
many years. This review presents a concise overview on supervised and unsupervised data …
[HTML][HTML] Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling
Radiation outcomes prediction (ROP) plays an important role in personalized prescription
and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation …
and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation …
[HTML][HTML] Develo** and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries
Purpose Tools for survival prediction for non-small cell lung cancer (NSCLC) patients
treated with chemoradiation or radiation therapy are of limited quality. In this work, we …
treated with chemoradiation or radiation therapy are of limited quality. In this work, we …
Decision support systems for personalized and participative radiation oncology
A paradigm shift from current population based medicine to personalized and participative
medicine is underway. This transition is being supported by the development of clinical …
medicine is underway. This transition is being supported by the development of clinical …