30 citations · 54 across the 7 of their papers we have counts for
7 papers
Tangent functional connectomes uncover more unique phenotypic traits
Kausar Abbas, Mintao Liu, Michael Wang +9
Functional connectomes (FCs) contain pairwise estimations of functional couplings based on pairs of brain regions activity. FCs are commonly represented as correlation matrices tha…
Unsupervised Probabilistic Models for Sequential Electronic Health Records
Alan D. Kaplan, John D. Greene, Vincent X. Liu +1
We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences…
Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data
Alan D. Kaplan, Qi Cheng, K. Aditya Mohan +12
Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflict…
Functional Connectome Fingerprint Gradients in Young Adults
Uttara Tipnis, Kausar Abbas, Elizabeth Tran +4
The assessment of brain fingerprints has emerged in the recent years as an important tool to study individual differences and to infer quality of neuroimaging datasets. Studies so…
AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning
K. Aditya Mohan, Alan D. Kaplan
We present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes…
Pulse Pileup Rejection Methods Using a Two-Component Gaussian Mixture Model for Fast Neutron Detection with Pulse Shape Discriminating Scintillator
Andrew Glenn, Qi Cheng, Alan D. Kaplan +1
Pulse shape discriminating scintillator materials in many cases allow the user to identify two basic kinds of pulses arising from two kinds of particles: neutrons and gammas. An un…