Data-centric artificial intelligence in oncology: a systematic review assessing data quality in machine learning models for head and neck cancer
Abstract Machine learning models have been increasingly considered to model head and
neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of …
neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of …
A systematic review of predictive models for hospital‐acquired pressure injury using machine learning
Y Zhou, X Yang, S Ma, Y Yuan, M Yan - Nursing open, 2023 - Wiley Online Library
Aims and objectives To summarize the use of machine learning (ML) for hospital‐acquired
pressure injury (HAPI) prediction and to systematically assess the performance and …
pressure injury (HAPI) prediction and to systematically assess the performance and …
Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP
RO Alabi, M Elmusrati, I Leivo, A Almangush… - Scientific Reports, 2023 - nature.com
Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and
neck cancers. Individual NPC patients may attain different outcomes. This study aims to …
neck cancers. Individual NPC patients may attain different outcomes. This study aims to …
Machine learning-based prediction of in-hospital mortality for post cardiovascular surgery patients admitting to intensive care unit: a retrospective observational cohort …
S Bi, S Chen, J Li, J Gu - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Background and objectives The acute physiology and chronic health evaluation-IV model
(APACHE-IV), and the sequential organ failure assessment (SOFA) score are two traditional …
(APACHE-IV), and the sequential organ failure assessment (SOFA) score are two traditional …
Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target
A Ajmal, HA Alkhatabi, RM Alreemi, MA Alamri… - BMC chemistry, 2024 - Springer
Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer
death globally. Approximately 12–14% of non-small cell lung cancers are caused by …
death globally. Approximately 12–14% of non-small cell lung cancers are caused by …
[HTML][HTML] Emerging landscape of circHIPK3 and its role in cancer and other diseases
Q Shao, Y Huang, C Zhang… - Molecular Medicine …, 2021 - spandidos-publications.com
Circular RNAs (circRNAs) are a special class of recently re‑discovered RNAs, which are
covalently closed ring RNA molecules. circRNAs have been reported to possess multiple …
covalently closed ring RNA molecules. circRNAs have been reported to possess multiple …
Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset
CC Chang, CH Chen, JG Hsieh, JH Jeng - Scientific reports, 2023 - nature.com
Efforts have been made to improve the risk stratification model for patients with diffuse large
B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using …
B-cell lymphoma (DLBCL). This study aimed to evaluate the disease prognosis using …
Progression-free survival prediction in patients with nasopharyngeal carcinoma after intensity-modulated radiotherapy: machine learning vs. traditional statistics
Background: The Cox proportional hazards (CPH) model is the most commonly used
statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine …
statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine …
Artificial Intelligance in Radiation Oncology
M Yakar, D Etiz - Artificial Intelligance in Medical Imaging, 2021 - avesis.ogu.edu.tr
Artificial intelligence (AI) is a computer science that tries to mimic human-like intelligence in
machines that use computer software and algorithms to perform specific tasks without direct …
machines that use computer software and algorithms to perform specific tasks without direct …
Comprehensive analysis of the associations between clinical factors and outcomes by machine learning, using post marketing surveillance data of cabazitaxel in …
H Kazama, O Kawaguchi, T Seto, K Suzuki… - BMC cancer, 2022 - Springer
Background We aimed to evaluate relationships between clinical outcomes and explanatory
variables by network clustering analysis using data from a post marketing surveillance …
variables by network clustering analysis using data from a post marketing surveillance …