Machine learning prediction models evaluate the interactions of known and novel risk factors for opioid use disorder (OUD) and overdose in Post-9/11 Veterans. Several machine learning classification-tree modeling approaches are used to develop predictor profiles of OUD and overdose. 

PredictMod uses artificial intelligence to determine if predictions can be made about diabetes based on the gut microbiome.

Machine learning is used to identify predictors of veterans' suicidal ideation. The relevant data come from a web-based survey of veterans’ experiences within three months of separation and every six months after for the first three years after leaving military service.

This model interprets various real time inputs in a diagnostic and predictive capacity in order to forewarn episodes of PTSD and suicidality, support early and accurate diagnosis of the same, and gain a better understanding of the short and long term effects of stress, especially in extreme situations, as it relates to the onset of PTSD.

Nediser is a continuously trained artificial intelligence “radiology resident” that assists radiologists in confirming the X-ray properties in their radiology reports.  Nediser can select normal templates, detect hardware, evaluate patella alignment and leg length and angle discrepancy, and measure Cobb angles.

This is an IRB-approved study which aims to examine machine learning approaches to predict health outcomes of VA patients.  It will focus on the prediction of Alzheimer's disease, rehospitalization, and Chlostridioides difficile infection.

Behavidence is a mental health tracking app. Veterans download the app onto their phone and it compares their phone usage to that of a digital phenotype that represents people with confirmed diagnosis of mental health conditions. 

Researchers are performing chart review to collect true/false positive annotations and construct a vector embedding of patient records, followed by similarity-based retrieval of unlabeled records "near" the labeled ones (semi-supervised approach). The aim is to use machine learning as a filter, after the rules-based retrieval, to improve specificity.

This collaborative effort focuses on developing a deep learning framework to predict the various patterns of dementia seen on MRI and EEG and explore the use of these imaging modalities as biomarkers for various dementias and epilepsy disorders.  The VA is performing retrospective chart review to achieve this.

The Digital Command Center seeks to consolidate all data in a medical center and apply predictive prescriptive analytics to allow leaders to better optimize hospital performance.