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Machine Learning for Health symposium 2022 -- Extended Abstract track
Antonio Parziale, Monica Agrawal, Shalmali Joshi +4
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28,…
Leveraging Time Irreversibility with Order-Contrastive Pre-training
Monica Agrawal, Hunter Lang, Michael Offin +2
Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques. To overcome the difficulties posed by a lack of labeled data,…
Fast, Structured Clinical Documentation via Contextual Autocomplete
Divya Gopinath, Monica Agrawal, Luke Murray +3
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical co…
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Alexander K. Lew, Monica Agrawal, David Sontag +1
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the ha…
Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, Jure Leskovec
The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions. However, a major consequence of polypharmacy is a m…