Collin M. Stultz

Orcid: 0000-0002-3415-242X

According to our database1, Collin M. Stultz authored at least 21 papers between 2007 and 2024.

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Bibliography

2024
Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models.
CoRR, 2024

Detecting QT prolongation From a Single-lead ECG With Deep Learning.
CoRR, 2024

2023
Event-Based Contrastive Learning for Medical Time Series.
CoRR, 2023

Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals.
Proceedings of the Machine Learning for Healthcare Conference, 2023

Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series.
Proceedings of the International Conference on Machine Learning, 2023

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

2022
Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling.
PLoS Comput. Biol., 2022

Data Augmentation for Electrocardiograms.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Feature Selection Based on Subpopulations and Propensity Score Matching: A Coronary Artery Disease Use Case using the UK Biobank.
Proceedings of the AMIA 2022, 2022

2021
Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling.
CoRR, 2021

Learning to predict with supporting evidence: applications to clinical risk prediction.
Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020
Identifying unreliable predictions in clinical risk models.
npj Digit. Medicine, 2020

Quantifying Common Support between Multiple Treatment Groups Using a Contrastive-VAE.
Proceedings of the Machine Learning for Health Workshop, 2020

2019
Generative Oversampling with a Contrastive Variational Autoencoder.
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

2016
Mollack: a web server for the automated creation of conformational ensembles for intrinsically disordered proteins.
Bioinform., 2016

Transferring Knowledge from Text to Predict Disease Onset.
Proceedings of the 1st Machine Learning in Health Care, 2016

2012
Efficient Construction of Disordered Protein Ensembles in a Bayesian Framework with Optimal Selection of Conformations.
Proceedings of the Biocomputing 2012: Proceedings of the Pacific Symposium, 2012

2010
Motif discovery in physiological datasets: A methodology for inferring predictive elements.
ACM Trans. Knowl. Discov. Data, 2010

2009
The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design.
J. Comput. Aided Mol. Des., 2009

2008
The Effect of a ΔK280 Mutation on the Unfolded State of a Microtubule-Binding Repeat in Tau.
PLoS Comput. Biol., 2008

2007
Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge.
EURASIP J. Adv. Signal Process., 2007


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