Search Results for author:
Found 11 papers, 5 papers with code
Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network
For baseline segmentation, LAS-Net achieved a mean Dice score of 0. 772.
Domain-adapted large language models for classifying nuclear medicine reports
Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression
Clustering time-series data in healthcare is crucial for clinical phenotyping to understand patients' disease progression patterns and to design treatment guidelines tailored to homogeneous patient subgroups.
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
Self-Supervision Enhanced Feature Selection with Correlated Gates
Discovering relevant input features for predicting a target variable is a key scientific question.
A Variational Information Bottleneck Approach to Multi-Omics Data Integration
Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns.
Non-slice 3-stranded pretzel knots
Greene-Jabuka and Lecuona confirmed the slice-ribbon conjecture for 3-stranded pretzel knots except for an infinite family $P(a,-a-2,-\frac{(a+1)^2}{2})$ where $a$ is an odd integer greater than $1$.
Geometric Topology 57K10, 57K31, 57K40, 57N70
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities).
Actor-Critic Approach for Temporal Predictive Clustering
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities, etc.).
A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks
Currently available survival analysis methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks.
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Estimating the individual treatment effect (ITE) from observational data is essential in medicine.
