255 citations · 369 across the 16 of their papers we have counts for
16 papers
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…
Sample-Specific Debiasing for Better Image-Text Models
Peiqi Wang, Yingcheng Liu, Ching-Yun Ko +4
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One…
Using Multiple Instance Learning to Build Multimodal Representations
Peiqi Wang, William M. Wells, Seth Berkowitz +2
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-moda…
RadTex: Learning Efficient Radiograph Representations from Text Reports
Keegan Quigley, Miriam Cha, Ruizhi Liao +4
Automated analysis of chest radiography using deep learning has tremendous potential to enhance the clinical diagnosis of diseases in patients. However, deep learning models typica…
Image Classification with Consistent Supporting Evidence
Peiqi Wang, Ruizhi Liao, Daniel Moyer +3
Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facil…
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…
Multimodal Representation Learning via Maximization of Local Mutual Information
Ruizhi Liao, Daniel Moyer, Miriam Cha +5
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn…
Secondary Use of Employee COVID-19 Symptom Reporting as Syndromic Surveillance as an Early Warning Signal of Future Hospitalizations
Steven Horng, Ashley O'Donoghue, Tenzin Dechen +6
Importance: Alternative methods for hospital utilization forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sou…
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Geeticka Chauhan, Ruizhi Liao, William Wells +6
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiog…
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Steven Horng, Ruizhi Liao, Xin Wang +3
Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Materials and Methods: In this retrospective study, 369,071 ch…
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…
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…
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
Ruizhi Liao, Jonathan Rubin, Grace Lam +5
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients. Accurate assessment of…
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Alistair E. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum +7
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation.…
Clinical Tagging with Joint Probabilistic Models
Yoni Halpern, Steven Horng, David Sontag
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method…
Anchored Discrete Factor Analysis
Yoni Halpern, Steven Horng, David Sontag
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every late…