AIMC Topic: Survival Analysis
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Survival parametric modeling for patients with heart failure based on Kernel learning.
BMC medical research methodology
Jan 11, 2025
Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by i...
Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.
Alzheimer's research & therapy
Dec 30, 2024
BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the mos...
TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.
Artificial intelligence in medicine
Dec 16, 2024
In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events...
Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease.
Journal of neurology
Dec 16, 2024
BACKGROUND: Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a surv...
Application of machine learning in breast cancer survival prediction using a multimethod approach.
Scientific reports
Dec 3, 2024
Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivotal measure in setting appropriate care plans. To the best of our knowledge, this study is pioneeri...
Predicting the time to get back to work using statistical models and machine learning approaches.
BMC medical research methodology
Nov 29, 2024
BACKGROUND: Whether machine learning approaches are superior to classical statistical models for survival analyses, especially in the case of lack of proportionality, is unknown.
Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.
Scientific reports
Nov 29, 2024
Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients ...
Interpretable machine learning for time-to-event prediction in medicine and healthcare.
Artificial intelligence in medicine
Nov 21, 2024
Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challen...
Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study.
SLAS technology
Nov 9, 2024
The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning ...
Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.
Computer methods and programs in biomedicine
Nov 8, 2024
BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.
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