Using CPRS and CDW data, artificial intelligence is used to predict biologic response to thiopurines among Veterans with irritable bowel disease.

A machine learning model is used to predict disease progression among veterans with hepatitis C virus.

The VA-DoE Suicide Exemplar project is currently utilizing artificial intelligence to improve VA's ability to identify Veterans at risk for suicide through three closely related projects that all involve collaborations with the Department of Energy.

Using electronic health records (EHR) (both structured and unstructured data) as  inputs, this tool outputs deep phenotypes and predictions of health outcomes including suicide death, opioid overdose, and decompensated outcomes of chronic diseases.

Using VA electronic clinical data, the Medication Safety (MedSafe) Clinical Decision Support (CDS) system analyzes current clinical management for diabetes, hypertension, and chronic kidney disease, and makes patient-specific, evidence-based recommendations to primary care providers.  The system uses knowledge bases that encode clinical practice guideline recommendations and an automated execution engine

Machine learning is used to improve treatment of functional problems in patients with peripheral artery disease (PAD). Previously collected biomechanics data is used to identify representative gait signatures of PAD to 1) determine the gait signatures of patients with PAD and 2) the ability of limb acceleration measurements to identify and model the meaningful biomechanics measures from PAD data.

A machine learning approach is used to build predictive models of perfusionists’ decision-making during critical situations that occur in the cardiopulmonary bypass phase of cardiac surgery. Results may inform future development of computerized clinical decision support tools to be embedded into the operating room, improving patient safety and surgical outcomes.

The SoKat Suicide Ideation Engine (SSIE) uses natural language processing (NLP) to improve identification of Veteran suicide ideation (SI) from survey data collected by the Office of Mental Health (OMH) Veteran Crisis Line (VCL) support team (VSignals).

Machine learning algorithms use EEG and video data from a VHA epilepsy monitoring unit in order to automatically identify seizures without human intervention.

AI is used to add value as a transactor for intelligent identity resolution and linking.  AI also has a domain cache function that can be used for both Clinical Decision Support and for intelligent state reconstruction over time and real-time discrepancy detection.  As a synchronizer, AI can perform intelligent propagation and semi-automated discrepancy resolution.  AI adapters can be used for