Search Results for author: Monica Agrawal

Found 14 papers, 4 papers with code

Machine Learning for Health symposium 2022 -- Extended Abstract track

no code implementations28 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.

Co-training Improves Prompt-based Learning for Large Language Models

no code implementations2 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.

Zero-Shot Learning

Leveraging Time Irreversibility with Order-Contrastive Pre-training

no code implementations4 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.

Self-Supervised Learning

Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative

no code implementations8 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

Robust Benchmarking for Machine Learning of Clinical Entity Extraction

1 code implementation31 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.

BIG-bench Machine Learning Entity Extraction using GAN

Fast, Structured Clinical Documentation via Contextual Autocomplete

1 code implementation29 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.

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming

1 code implementation23 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.

Probabilistic Programming

Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph

no code implementations2 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.

TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes

no code implementations30 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.

Modeling polypharmacy side effects with graph convolutional networks

1 code implementation2 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.

Link Prediction

Large-scale analysis of disease pathways in the human interactome

no code implementations3 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.

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