no code implementations • 28 Nov 2022 • Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy
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, 2022, in New Orleans, Louisiana, USA.
no code implementations • 19 Oct 2022 • Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David Sontag
We study the application of large language models to zero-shot and few-shot classification of tabular data.
no code implementations • 25 May 2022 • Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David Sontag
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes.
no code implementations • 2 Feb 2022 • Hunter Lang, Monica Agrawal, Yoon Kim, David Sontag
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data.
no code implementations • 4 Nov 2021 • Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David Sontag
Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques.
no code implementations • 8 Mar 2021 • Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David Sontag
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most.
Decision Making
Human-Computer Interaction
1 code implementation • 31 Jul 2020 • Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David Sontag
We reformulate the annotation framework for clinical entity extraction to factor in these issues to allow for robust end-to-end system benchmarking.
1 code implementation • 29 Jul 2020 • Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.
1 code implementation • 23 Jul 2020 • Alexander K. Lew, Monica Agrawal, David Sontag, Vikash K. Mansinghka
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 hardness of inference make Bayesian approaches difficult to automate.
no code implementations • 13 Jan 2020 • Benjamin Birnbaum, Nathan Nussbaum, Katharina Seidl-Rathkopf, Monica Agrawal, Melissa Estevez, Evan Estola, Joshua Haimson, Lucy He, Peter Larson, Paul Richardson
Objective Electronic health records (EHRs) are a promising source of data for health outcomes research in oncology.
no code implementations • 2 Oct 2019 • Irene Y. Chen, Monica Agrawal, Steven Horng, David Sontag
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.
no code implementations • 30 Nov 2018 • Monica Agrawal, Griffin Adams, Nathan Nussbaum, Benjamin Birnbaum
In this work, we present TIFTI (Temporally Integrated Framework for Treatment Intervals), a robust framework for extracting oral drug treatment intervals from a patient's unstructured notes.
1 code implementation • 2 Feb 2018 • Marinka Zitnik, Monica Agrawal, Jure Leskovec
The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type.
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no code implementations • 3 Dec 2017 • Monica Agrawal, Marinka Zitnik, Jure Leskovec
However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.