Research Considerations Archives

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans Back Abstract Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been…

Opening the Duke electronic health record to apps: Implementing SMART on FHIR

Back Opening the Duke electronic health record to apps: Implementing SMART on FHIR Highlights • The SMART on FHIR framework is a novel tool for EHR interoperability. • A custom integration of SMART on FHIR with the Epic EHR is demonstrated. • Several provider and patient apps are successfully integrated using this technique. • Security…

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Back to homepage Abstract Final version accepted 12 January 2021 Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19…

Federated learning allows hospitals to share data privately

Researchers have shown that federated learning is successful in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions. The approach can be used to create an Artificial Intelligence system that will help clinicians better identify and treat…