Search Results for author:
Found 8 papers, 4 papers with code
Multimodal hierarchical multi-task deep learning framework for jointly predicting and explaining Alzheimer disease progression
We proposed a multimodal hierarchical multi-task learning approach which can monitor the risk of disease progression at each timepoint of the visit trajectory.
Prescribing Large Language Models for Perioperative Care: What's The Right Dose for Pre-trained Models?
Adapting models further improved performance: (1) self-supervised finetuning by 3. 2% for AUROC and 1. 5% for AUPRC; (2) semi-supervised finetuning by 1. 8% for AUROC and 2% for AUPRC, compared to self-supervised finetuning; (3) foundational modelling by 3. 6% for AUROC and 2. 6% for AUPRC, compared to self-supervised finetuning.
Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs
EHR audit logs are a highly granular stream of events that capture clinician activities, and is a significant area of interest for research in characterizing clinician workflow on the electronic health record (EHR).
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection
A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome.
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records
Burnout is a significant public health concern affecting nearly half of the healthcare workforce.
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
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 for clinicians and healthcare providers.
Surgical Prediction with Interpretable Latent Representation
Given the risks and cost of surgeries, there has been significant interest in exploiting predictive models to improve perioperative care.
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks
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 patient harm during surgery.
