Search Results for author:
Found 5 papers, 2 papers with code
Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
The lack of focus on missingness representation in an individualized way limits the full utilization of machine learning applications towards true personalization.
A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data
To this end, this work provides an overview of ITE works for time-series data and insights into future research.
IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records
In IGNITE, we further propose a novel individualized missingness mask (IMM), which helps our model generate values based on the individual's observed data and missingness patterns.
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data.
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study
In test set B (225 patient encounters), the respective system achieves 0. 90 AUROC for AKI, elevated troponin, and elevated interleukin-6, and >0. 80 AUROC for most of the other complications.
