The application of deep learning in cancer prognosis prediction
Deep learning has been applied to many areas in health care, including imaging diagnosis,
digital pathology, prediction of hospital admission, drug design, classification of cancer and …
digital pathology, prediction of hospital admission, drug design, classification of cancer and …
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 …
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Clinical trials are indispensable for medical research and the development of new
treatments. However, clinical trials often involve thousands of participants and can span …
treatments. However, clinical trials often involve thousands of participants and can span …
Deep learning for the partially linear Cox model
Deep learning for the partially linear Cox model Page 1 The Annals of Statistics 2022, Vol. 50,
No. 3, 1348–1375 https://doi.org/10.1214/21-AOS2153 © Institute of Mathematical Statistics …
No. 3, 1348–1375 https://doi.org/10.1214/21-AOS2153 © Institute of Mathematical Statistics …
Bias in cross-entropy-based training of deep survival networks
SG Zadeh, M Schmid - IEEE transactions on pattern analysis …, 2020 - ieeexplore.ieee.org
Over the last years, utilizing deep learning for the analysis of survival data has become
attractive to many researchers. This has led to the advent of numerous network architectures …
attractive to many researchers. This has led to the advent of numerous network architectures …
Adoption of machine learning in pharmacometrics: an overview of recent implementations and their considerations
A Janssen, FC Bennis, RAA Mathôt - Pharmaceutics, 2022 - mdpi.com
Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology,
pharmacology, and disease to describe and quantify the interactions between medication …
pharmacology, and disease to describe and quantify the interactions between medication …
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 …
Survite: Learning heterogeneous treatment effects from time-to-event data
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
While both the related problems of (i) estimating treatment effects for binary or continuous …
While both the related problems of (i) estimating treatment effects for binary or continuous …
Towards generating real-world time series data
Time series data generation has drawn increasing attention in recent years. Several
generative adversarial network (GAN) based methods have been proposed to tackle the …
generative adversarial network (GAN) based methods have been proposed to tackle the …
A hierarchical career-path-aware neural network for job mobility prediction
The understanding of job mobility can benefit talent management operations in a number of
ways, such as talent recruitment, talent development, and talent retention. While there is …
ways, such as talent recruitment, talent development, and talent retention. While there is …