260 citations · 418 across the 14 of their papers we have counts for
14 papers
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis
Juan Trelles, Andrew Wentzel, William Berrios +1
In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image label…
A Method for Quantifying Position Reconstruction Uncertainty in Astroparticle Physics using Bayesian Networks
Christina Peters, Aaron Higuera, Shixiao Liang +4
Robust position reconstruction is paramount for enabling discoveries in astroparticle physics as backgrounds are significantly reduced by only considering interactions within the f…
Domain-informed neural networks for interaction localization within astroparticle experiments
Shixiao Liang, Aaron Higuera, Christina Peters +4
This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) t…
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)
Moumita Bhattacharya, Dai-Yin Lu, Shibani M Kudchadkar +10
Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart…
Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model
Moumita Bhattacharya, Dai-Yin Lu, Ioannis Ventoulis +9
Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low risk of congestive heart failure, hypertension…
Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes
Moumita Bhattacharya, Claudine Jurkovitz, Hagit Shatkay
Patients associated with multiple co-occurring health conditions often face aggravated complications and less favorable outcomes. Co-occurring conditions are especially prevalent a…
Approximate Queries and Representations for Large Data Sequences
Hagit Shatkay, Stanley B. Zdonik
Many new database application domains such as experimental sciences and medicine are characterized by large sequences as their main form of data. Using approximate representation c…
Assessing Chronic Kidney Disease from Office Visit Records Using Hierarchical Meta-Classification of an Imbalanced Dataset
M. Bhattacharya, C. Jurkovitz, H. Shatkay
Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting 13% of the US population. The disease is often a silent condition, making its diagnosis challenging. I…
Identifying Patterns of Associated-Conditions through Topic Models of Electronic Medical Records
Moumita Bhattacharya, Claudine Jurkovitz, Hagit Shatkay
Multiple adverse health conditions co-occurring in a patient are typically associated with poor prognosis and increased office or hospital visits. Developing methods to identify pa…
Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records
Moumita Bhattacharya, Deborah Ehrenthal, Hagit Shatkay
Obesity is one of the leading health concerns in the United States. Researchers and health care providers are interested in understanding factors affecting obesity and detecting th…
Protein (Multi-)Location Prediction: Using Location Inter-Dependencies in a Probabilistic Framework
Ramanuja Simha, Hagit Shatkay
Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has b…
Learning Hidden Markov Models with Geometrical Constraints
Hagit Shatkay
Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) form a useful tool for modeling dynamical systems. They are particularly useful for represen…
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
L. P. Kaelbling, H. Shatkay
Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for repres…
A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature
Anália Lourenço, Michael Conover, Andrew Wong +4
We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challe…