22 citations · 47 across the 6 of their papers we have counts for
8 papers
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…
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…