33 citations · 73 across the 11 of their papers we have counts for
11 papers
EHR-RAGp: Prototype-Guided Retrieval of Longitudinal Electronic Health Records for Clinical Prediction Models
Saeed Shurrab, Mariam Al-Omari, Dana El Samad +1
Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effectively leveraging historical da…
Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
L. Julián Lechuga López, Tim G. J. Rudner, Farah E. Shamout
Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trustworthiness is to enable models t…
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
Yiqiu Shen, Jungkyu Park, Frank Yeung +4
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models a…
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout
Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "pai…
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in deci…
Meta-repository of screening mammography classifiers
Benjamin Stadnick, Jan Witowski, Vishwaesh Rajiv +4
Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer d…
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
Nasir Hayat, Hazem Lashen, Farah E. Shamout
Despite the success of deep neural networks in chest X-ray (CXR) diagnosis, supervised learning only allows the prediction of disease classes that were seen during training. At inf…
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
Anuroop Sriram, Matthew Muckley, Koustuv Sinha +7
The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. M…
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study
Ghadeer O. Ghosheh, Bana Alamad, Kai-Wen Yang +10
Existing prognostic tools mainly focus on predicting the risk of mortality among patients with coronavirus disease 2019. However, clinical evidence suggests that COVID-19 can resul…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…