On the challenges and opportunities in generative ai
The field of deep generative modeling has grown rapidly and consistently over the years.
With the availability of massive amounts of training data coupled with advances in scalable …
With the availability of massive amounts of training data coupled with advances in scalable …
Counterfactual phenotyping with censored time-to-events
Estimation of treatment efficacy of real-world clinical interventions involves working with
continuous time-to-event outcomes such as time-to-death, re-hospitalization, or a composite …
continuous time-to-event outcomes such as time-to-death, re-hospitalization, or a composite …
SurvivalLVQ: Interpretable supervised clustering and prediction in survival analysis via Learning Vector Quantization
Identifying subgroups with similar survival outcomes is a pivotal challenge in survival
analysis. Traditional clustering methods often neglect the outcome variable, potentially …
analysis. Traditional clustering methods often neglect the outcome variable, potentially …
Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering
Survival analysis involves the modelling of the times to event. Proposed neural network
approaches maximise the predictive performance of traditional survival models at the cost of …
approaches maximise the predictive performance of traditional survival models at the cost of …
Variable selection for nonlinear cox regression model via deep learning
K Li - arXiv preprint arXiv:2211.09287, 2022 - arxiv.org
Variable selection problem for the nonlinear Cox regression model is considered. In survival
analysis, one main objective is to identify the covariates that are associated with the risk of …
analysis, one main objective is to identify the covariates that are associated with the risk of …
[HTML][HTML] Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction
Image texture analysis has for decades represented a promising opportunity for cancer
assessment and disease progression evaluation, evolving in a discipline, ie, radiomics …
assessment and disease progression evaluation, evolving in a discipline, ie, radiomics …
[HTML][HTML] Intention-guided deep semi-supervised document clustering via metric learning
L Jingnan, L Chuan, H Ruizhang, Q Yongbin… - Journal of King Saud …, 2023 - Elsevier
The intention expresses the user's preference for document structure division. Intention-
guided document structure division is an important task in the field of text mining. To achieve …
guided document structure division is an important task in the field of text mining. To achieve …
Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery
Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted
millions of people worldwide. The neuroanatomical heterogeneity of AD has made it …
millions of people worldwide. The neuroanatomical heterogeneity of AD has made it …
SurvSHAP: a proxy-based algorithm for explaining survival models with SHAP
A Alabdallah, S Pashami… - 2022 IEEE 9th …, 2022 - ieeexplore.ieee.org
Survival Analysis models usually output functions (survival or hazard functions) rather than
point predictions like regression and classification models. This makes the explanations of …
point predictions like regression and classification models. This makes the explanations of …
Estimating treatment effects from single-arm trials via latent-variable modeling
M Haussmann, TMS Le, V Halla-aho… - International …, 2024 - proceedings.mlr.press
Randomized controlled trials (RCTs) are the accepted standard for treatment effect
estimation but they can be infeasible due to ethical reasons and prohibitive costs. Single …
estimation but they can be infeasible due to ethical reasons and prohibitive costs. Single …