Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models
Introduction Machine learning has been increasingly used to develop predictive models to
diagnose different disease conditions. The heterogeneity of the kidney transplant population …
diagnose different disease conditions. The heterogeneity of the kidney transplant population …
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
S Pölsterl - Journal of Machine Learning Research, 2020 - jmlr.org
scikit-survival is an open-source Python package for time-to-event analysis fully compatible
with scikit-learn. It provides implementations of many popular machine learning techniques …
with scikit-learn. It provides implementations of many popular machine learning techniques …
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
A Moncada-Torres, MC van Maaren, MP Hendriks… - Scientific reports, 2021 - nature.com
Abstract Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in
oncology. Recently, several machine learning (ML) techniques have been adapted for this …
oncology. Recently, several machine learning (ML) techniques have been adapted for this …
Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound
Recent findings Cardiovascular disease (CVD) is the leading cause of mortality and poses
challenges for healthcare providers globally. Risk-based approaches for the management of …
challenges for healthcare providers globally. Risk-based approaches for the management of …
Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data
As a highly contagious respiratory disease, COVID-19 has yielded high mortality rates since
its emergence in December 2019. As the number of COVID-19 cases soars in epicenters …
its emergence in December 2019. As the number of COVID-19 cases soars in epicenters …
Survival prediction of heart failure patients using motion-based analysis method
Abstract Background and Objective: Survival prediction of heart failure patients is critical to
improve the prognostic management of the cardiovascular disease. The existing survival …
improve the prognostic management of the cardiovascular disease. The existing survival …
On the use of Harrell's C for clinical risk prediction via random survival forests
M Schmid, MN Wright, A Ziegler - Expert Systems with Applications, 2016 - Elsevier
Random survival forests (RSF) are a powerful method for risk prediction of right-censored
outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of …
outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of …
Survtrace: Transformers for survival analysis with competing events
In medicine, survival analysis studies the time duration to events of interest such as mortality.
One major challenge is how to deal with multiple competing events (eg, multiple disease …
One major challenge is how to deal with multiple competing events (eg, multiple disease …
Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals
T Minami, M Sato, H Toyoda, S Yasuda, T Yamada… - Journal of …, 2023 - Elsevier
Background & Aims Accurate risk stratification for hepatocellular carcinoma (HCC) following
the achievement of a sustained virologic response (SVR) is necessary for optimal …
the achievement of a sustained virologic response (SVR) is necessary for optimal …
High-dimensional survival analysis: Methods and applications
In the era of precision medicine, time-to-event outcomes such as time to death or
progression are routinely collected, along with high-throughput covariates. These high …
progression are routinely collected, along with high-throughput covariates. These high …