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Soft-tissue sarcomas (STSs) of the extremities are a group of malignancies arising from the mesenchymal cells that may develop distant metastases or local recurrence. In this article, we propose a novel methodology aimed to predict metastases and recurrence risk in patients with these malignancies by evaluating magnetic resonance radiomic features that will be formally verified through formal logic models.

Our objective is to assess the accuracy of the COVID-19 vaccination status within the electronic health record (EHR) for a panel of patients in a primary care practice when manual queries of the state immunization databases are required to access outside immunization records.

Evaluate self-reported electronic screening (eScreening) in a VA Transition Care Management Program (TCM) to improve the accuracy and completeness of administrative ethnicity and race data.

Older adults with multimorbidities have the highest rate of emergency department (ED) usage. These patients are typically on numerous medications, may have underlying dementia, and often present with falls and delirium. Identifying these high-risk older adults for possible intervention is challenging in the ED setting since available screening methods are manual and resource-intensive. The objective is to study the electronic medical record (EMR) use for identifying high-risk older adults in [...]

To assess whether a fall-prevention clinical decision support (CDS) approach using electronic analytics that stimulates risk-targeted interventions is associated with reduced rates of falls and injurious falls.

With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus [...]

Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports.

This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability.

The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing.

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient [...]