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Deep learning for survival analysis: a review
The influx of deep learning (DL) techniques into the field of survival analysis in recent years
has led to substantial methodological progress; for instance, learning from unstructured or …
has led to substantial methodological progress; for instance, learning from unstructured or …
Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome
characterized by cognitive impairment severe enough to interfere with activities of daily life …
characterized by cognitive impairment severe enough to interfere with activities of daily life …
A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
Data collected from clinical trials and cohort studies, such as dementia studies, are often
high-dimensional, censored, heterogeneous and contain missing information, presenting …
high-dimensional, censored, heterogeneous and contain missing information, presenting …
Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany
During 2020, the infection rate of COVID-19 has been investigated by many scholars from
different research fields. In this context, reliable and interpretable forecasts of disease …
different research fields. In this context, reliable and interpretable forecasts of disease …
DAFT: A universal module to interweave tabular data and 3D images in CNNs
Prior work on Alzheimer's Disease (AD) has demonstrated that convolutional neural
networks (CNNs) can leverage the high-dimensional image information for diagnosing …
networks (CNNs) can leverage the high-dimensional image information for diagnosing …
Combining 3d image and tabular data via the dynamic affine feature map transform
Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain
established that convolutional neural networks (CNNs) can leverage the high-dimensional …
established that convolutional neural networks (CNNs) can leverage the high-dimensional …
Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis
Abstract Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous
factors, and it is difficult to predict individual progression trajectory from normal or mildly …
factors, and it is difficult to predict individual progression trajectory from normal or mildly …
Deep extended hazard models for survival analysis
Unlike standard prediction tasks, survival analysis requires modeling right censored data,
which must be treated with care. While deep neural networks excel in traditional supervised …
which must be treated with care. While deep neural networks excel in traditional supervised …
A new PHO-rmula for improved performance of semi-structured networks
D Rügamer - International Conference on Machine Learning, 2023 - proceedings.mlr.press
Recent advances to combine structured regression models and deep neural networks for
better interpretability, more expressiveness, and statistically valid uncertainty quantification …
better interpretability, more expressiveness, and statistically valid uncertainty quantification …
Bayesian Semi-structured Subspace Inference
Semi-structured regression models enable the joint modeling of interpretable structured and
complex unstructured feature effects. The structured model part is inspired by statistical …
complex unstructured feature effects. The structured model part is inspired by statistical …