6 citations · 16 across the 18 of their papers we have counts for
19 papers
Local conjugacy in prosolvable groups
Michael C. Burkhart
For prosolvable groups, we provide conditions under which two locally conjugate supplements of a normal pronilpotent subgroup are conjugate, analogous to those given by Losey and S…
Federated generative event models for tokenized electronic health records
Michael C. Burkhart, Luke Solo, Inhyeok Lee +8
Electronic health record foundation models are limited by institutionally siloed data and substantial performance degradation under cross-site transfer. We evaluated federated trai…
Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization
Inhyeok Lee, Luke Solo, Michael C. Burkhart +5
Generative medical event models use tokenized sequences of patient timelines as input, but practical guidance on the many decisions around tokenization is limited. We benchmark qua…
Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators
Luke Solo, Matthew B. A. McDermott, William F. Parker +3
Generative foundation models trained on tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction via Monte Carlo sampling of simulated future…
Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
Sahil Sethi, David Chen, Michael C. Burkhart +3
Prototype-based neural networks offer interpretable predictions by comparing inputs to learned, representative signal patterns anchored in training data. While such models have sho…
Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models
Michael C. Burkhart, Bashar Ramadan, Luke Solo +2
We present a foundation model-derived method to identify highly informative tokens and events in electronic health records. Our approach considers incoming data in the entire conte…
Foundation models for electronic health records: representation dynamics and transferability
Michael C. Burkhart, Bashar Ramadan, Zewei Liao +4
Foundation models (FMs) trained on electronic health records (EHRs) have shown strong performance on a range of clinical prediction tasks. However, adapting these models to local h…
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Sahil Sethi, David Chen, Thomas Statchen +4
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanatio…
Actions of nilpotent groups on nilpotent groups
Michael C. Burkhart
For finite nilpotent groups and , suppose acts on via automorphisms. We exhibit a decomposition of the first cohomology set in terms of the first cohomologies of the…
Fixed point conditions for non-coprime actions
Michael C. Burkhart
In the setting of finite groups, suppose acts on via automorphisms so that the induced semidirect product acts on some non-empty set , with acting trans…
Safe AI for health and beyond -- Monitoring to transform a health service
Mahed Abroshan, Michael Burkhart, Oscar Giles +7
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems oft…
Conjugacy conditions for supersoluble complements of an abelian base and a fixed point result for non-coprime actions
Michael C. Burkhart
We demonstrate that two supersoluble complements of an abelian base in a finite split extension are conjugate if and only if, for each prime , a Sylow -subgroup of one comple…
Deep Low-Density Separation for Semi-Supervised Classification
Michael C. Burkhart, Kyle Shan
Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create…
Neuroevolutionary Feature Representations for Causal Inference
Michael C. Burkhart, Gabriel Ruiz
Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature…
Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions
Michael C. Burkhart
To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient a…
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Michael C. Burkhart
Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical f…
The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering
Michael C. Burkhart, David M. Brandman, Carlos E. Vargas-Irwin +1
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model. The model requires a linear relationsh…
Conditional Probabilities of Multivariate Poisson Distributions
Michael C. Burkhart
Multivariate Poisson distributions have numerous applications. Fast computation of these distributions, holding constant a fixed set of linear combinations of these variables, has…
Linear Transformations & the Multivariate Generating Function
Michael C. Burkhart
This note examines linear combinations of multi-indexed sequences and derives the multivariate generating function of such a linear combination in terms of the original sequence's…