38 citations · 63 across the 5 of their papers we have counts for
6 papers
A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients
Junbo Shen, Bing Xue, Thomas Kannampallil +2
Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for…
The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes
Charles Alba, Bing Xue, Joanna Abraham +2
Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this g…
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection
Benjamin C. Warner, Ziqi Xu, Simon Haroutounian +2
Survey data can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data u…
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records
Hanyang Liu, Sunny S. Lou, Benjamin C. Warner +3
Burnout is a significant public health concern affecting nearly half of the healthcare workforce. This paper presents the first end-to-end deep learning framework for predicting ph…
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Sayantan Kumar, Sean C. Yu, Thomas Kannampallil +3
Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions fo…
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks
Hanyang Liu, Michael C. Montana, Dingwen Li +3
We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious pati…