678 citations · 2.6k across the 71 of their papers we have counts for
14 papers · 1 filter
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
Limeng Cui, Siddharth Biswal, Lucas M. Glass +3
Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and…
CORE: Automatic Molecule Optimization Using Copy & Refine Strategy
Tianfan Fu, Cao Xiao, Jimeng Sun
Molecule optimization is about generating molecule with more desirable properties based on an input molecule . The state-of-the-art approaches partition the molecules into a…
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment
Siddharth Biswal, Cao Xiao, Lucas M. Glass +2
Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor repr…
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Kexin Huang, Cao Xiao, Trong Nghia Hoang +2
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and soci…
CUP: Cluster Pruning for Compressing Deep Neural Networks
Rahul Duggal, Cao Xiao, Richard Vuduc +1
We propose Cluster Pruning (CUP) for compressing and accelerating deep neural networks. Our approach prunes similar filters by clustering them based on features derived from both t…
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
Ardavan Afshar, Ioakeim Perros, Haesun Park +5
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of p…
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover +1
Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram…
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Tengfei Ma, Junyuan Shang, Cao Xiao +1
Gaining more comprehensive knowledge about drug-drug interactions (DDIs) is one of the most important tasks in drug development and medical practice. Recently graph neural networks…
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model
Fan Zhang, Tong Wu, Yunlong Wang +5
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the comm…
Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
Kezi Yu, Yunlong Wang, Yong Cai +4
Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an im…
Causal Regularization
Dominik Janzing
I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample r…
Clinical Concept Extraction for Document-Level Coding
Sarah Wiegreffe, Edward Choi, Sherry Yan +2
The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been infor…
Pre-training of Graph Augmented Transformers for Medication Recommendation
Junyuan Shang, Tengfei Ma, Cao Xiao +1
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal elec…
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Shenda Hong, Cao Xiao, Tengfei Ma +2
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however t…