Research

Our Lab GitHub


DISCERN criteria

Auto-Discern

We are building a machine learning system that rates the quality of health information websites, such as WebMD. We use the DISCERN instrument, which evaluates articles on attributes such as “are the treatment risks described” and “are the sources cited”.

View the code on GitHub


30 days all-cause readmission

30 days all-cause readmission

Abstract Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1) explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2) to examine the additive value of patients’ hospitalization timelines on prediction performance.

Presentation

ECCB 2018 poster can be downloaded here

Paper got accepted in Scientific Reports - Nature

View the source code