Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

Purpose: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System.

Methods: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process.

An application to support COVID-19 occupational health and patient tracking at a Veterans Affairs medical center.

Objective: Reducing risk of coronavirus disease 2019 (COVID-19) infection among healthcare personnel requires a robust occupational health response involving multiple disciplines. We describe a flexible informatics solution to enable such coordination, and we make it available as open-source software.

Materials And Methods: We developed a stand-alone application that integrates data from several sources, including electronic health record data and data captured outside the electronic health record.

Hypersphere clustering to characterize healthcare providers using prescriptions and procedures from Medicare claims data.

We consider the task of producing a useful clustering of healthcare providers from their clinical action signature- their drug, procedure, and billing codes. Because high-dimensional sparse count vectors are challenging to cluster, we develop a novel autoencoder framework to address this task. Our solution creates a low-dimensional embedded representation of the high-dimensional space that preserves angular relationships and assigns examples to clusters while optimizing the quality of this clustering.

Case misclassification in studies of spinal manipulation and arterial dissection.

Background: Spinal manipulation has been associated with cervical arterial dissection and stroke but a causal relationship has been questioned by population-based studies. Earlier studies identified cases using International Classification of Diseases Ninth Revision (ICD-9) codes specific to anatomic stroke location rather than stroke etiology. We hypothesize that case misclassification occurred in these previous studies and an underestimation of the strength of the association.