1.5k citations · 1.9k across the 18 of their papers we have counts for
19 papers
Diagnosing our datasets: How does my language model learn clinical information?
Furong Jia, David Sontag, Monica Agrawal
Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) dat…
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models
Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse +3
Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the…
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study
Niklas Mannhardt, Elizabeth Bondi-Kelly, Barbara Lam +10
Large language models (LLMs) have immense potential to make information more accessible, particularly in medicine, where complex medical jargon can hinder patient comprehension of…
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
Sharon Jiang, Shannon Shen, Monica Agrawal +5
The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively…
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,…
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang +3
We study the application of large language models to zero-shot and few-shot classification of tabular data. We prompt the large language model with a serialization of the tabular d…
Large Language Models are Few-Shot Clinical Information Extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang +2
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the gener…
Co-training Improves Prompt-based Learning for Large Language Models
Hunter Lang, Monica Agrawal, Yoon Kim +1
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data. While prompting has emerged as a promising par…
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,…
MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records
Luke Murray, Divya Gopinath, Monica Agrawal +3
Clinical documentation can be transformed by Electronic Health Records, yet the documentation process is still a tedious, time-consuming, and error-prone process. Clinicians are fa…
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative
Ariel Levy, Monica Agrawal, Arvind Satyanarayan +1
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most. However, a key concern is whether users overly trust or cede age…
Robust Benchmarking for Machine Learning of Clinical Entity Extraction
Monica Agrawal, Chloe O'Connell, Yasmin Fatemi +2
Clinical studies often require understanding elements of a patient's narrative that exist only in free text clinical notes. To transform notes into structured data for downstream u…
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…
Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research
Benjamin Birnbaum, Nathan Nussbaum, Katharina Seidl-Rathkopf +7
Objective Electronic health records (EHRs) are a promising source of data for health outcomes research in oncology. A challenge in using EHR data is that selecting cohorts of patie…
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Irene Y. Chen, Monica Agrawal, Steven Horng +1
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph cou…
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes
Monica Agrawal, Griffin Adams, Nathan Nussbaum +1
Oral drugs are becoming increasingly common in oncology care. In contrast to intravenous chemotherapy, which is administered in the clinic and carefully tracked via structure elect…
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…
Large-scale analysis of disease pathways in the human interactome
Monica Agrawal, Marinka Zitnik, Jure Leskovec
Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actiona…