Mind the Performance Gap: Examining Dataset Shift During Prospective Validation

no code implementations • 23 Jul 2021 • Erkin Ötleş, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens

In this study, we compare the 2020-2021 ('20-'21) prospective performance of a patient risk stratification model for predicting healthcare-associated infections to a 2019-2020 ('19-'20) retrospective validation of the same model.

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series

1 code implementation • Machine Learning for Healthcare 2019 2019 • Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael Sjoding, Jenna Wiens

In settings with limited data, relaxed parameter sharing can lead to improved patient risk stratification performance.

Respiratory Failure Time Series +1

Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks

no code implementations • 21 Aug 2018 • Jeeheh Oh, Jiaxuan Wang, Jenna Wiens

Recently, researchers have started applying convolutional neural networks (CNNs) with one-dimensional convolutions to clinical tasks involving time-series data.

Computational Efficiency Dynamic Time Warping +2

Learning Credible Models

1 code implementation • 8 Nov 2017 • Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens

In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible.