Machine and deep learning for longitudinal biomedical data: a review of methods and applications

A Cascarano, J Mur-Petit… - Artificial Intelligence …, 2023 - Springer
Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical
field, as many diseases have a complex and multi-factorial time-course, and start to develop …

Deletion of ferritin H in neurons counteracts the protective effect of melatonin against traumatic brain injury‐induced ferroptosis

T Rui, H Wang, Q Li, Y Cheng, Y Gao… - Journal of Pineal …, 2021 - Wiley Online Library
Accumulating evidence demonstrates that ferroptosis may be important in the
pathophysiological process of traumatic brain injury (TBI). As a major hormone of the pineal …

Mechanism of ferroptosis and its relationships with other types of programmed cell death: insights for potential therapeutic benefits in traumatic brain injury

Q Pang, L Zheng, Z Ren, H Xu, H Guo… - Oxidative Medicine …, 2022 - Wiley Online Library
Traumatic brain injury (TBI) is a serious health issue with a high incidence, high morbidity,
and high mortality that poses a large burden on society. Further understanding of the …

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

H Khalili, M Rismani, MA Nematollahi, MS Masoudi… - Scientific reports, 2023 - nature.com
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging
worldwide. The present study aimed to achieve the most accurate machine learning (ML) …

The application of data mining techniques to oral cancer prognosis

WT Tseng, WF Chiang, SY Liu, J Roan… - Journal of medical …, 2015 - Springer
This study adopted an integrated procedure that combines the clustering and classification
features of data mining technology to determine the differences between the symptoms …

Mortality prediction in severe traumatic brain injury using traditional and machine learning algorithms

X Wu, Y Sun, X Xu, EW Steyerberg… - Journal of …, 2023 - liebertpub.com
Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision
and health care policy making. This study aimed to develop and validate prediction models …

[HTML][HTML] Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm

A Monsalve-Torra, D Ruiz-Fernandez… - Journal of biomedical …, 2016 - Elsevier
An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal
level. This disease presents high rate of mortality and complications causing a decrease in …

The detection of mild traumatic brain injury in paediatrics using artificial neural networks

H Ellethy, SS Chandra, FA Nasrallah - Computers in Biology and Medicine, 2021 - Elsevier
Head computed tomography (CT) is the gold standard in emergency departments (EDs) to
evaluate mild traumatic brain injury (mTBI) patients, especially for paediatrics. Data-driven …

Machine learning in the prediction of trauma outcomes: a systematic review

T Zhang, A Nikouline, D Lightfoot, B Nolan - Annals of emergency medicine, 2022 - Elsevier
Study objective Machine learning models carry unique potential as decision-making aids
and prediction tools for improving patient care. Traumatically injured patients provide a …

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

LM Bull, M Lunt, GP Martin, K Hyrich… - … and prognostic research, 2020 - Springer
Abstract Background Clinical prediction models (CPMs) predict the risk of health outcomes
for individual patients. The majority of existing CPMs only harness cross-sectional patient …