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To develop and externally validate machine learning models using structured and unstructured electronic health record data to predict postoperative acute kidney injury (AKI) across inpatient settings.

Present an artificial intelligence-enabled pipeline for estimating the prevalence of depression and general anxiety among pregnant women using texts from their social media posts. Use said pipeline to analyze mental health trends on subreddits frequented by pregnant women and report on interesting insights that could be helpful for policy-makers, clinicians, etc.

Remote monitoring of women experiencing hypertensive disorders of pregnancy (HDP) can provide timely life-saving data, particularly if these data are integrated into existing patient and clinical workflows. This pilot intervention of a smartphone application (app) for postpartum monitoring of hypertensive disorders integrates patient-contributed data into electronic health records (EHRs) to support monitoring and clinical decision-making. Results from the evaluation of the pilot highlight the resources needed when implementing the app [...]

Substance misuse is a complex and heterogeneous set of conditions associated with high mortality and regional/demographic variations. Existing data systems are siloed and have been ineffective in curtailing the substance misuse epidemic. Therefore, we aimed to build a novel informatics platform, the Substance Misuse Data Commons (SMDC), by integrating multiple data modalities to provide a unified record of information crucial to improving outcomes in substance misuse patients.

Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods.

Efficiently identifying the social risks of patients with serious illnesses (SIs) is the critical first step in providing patient-centered and value-driven care for this medically vulnerable population.

Latinx people comprise 30% of all new human immunodeficiency virus (HIV) infections in the United States and face many challenges to accessing and engaging with HIV care. To bridge these gaps in care, a Spanish-language mobile health (mHealth) intervention known as ConexionesPositivas (CP) was adapted from an established English-language platform called PositiveLinks (PL) to help improve engagement in care and reduce viral nonsuppression among its users. We aimed to determine [...]

To analyze PeriData.Net, a clinical registry with linked maternal-infant hospital data of Milwaukee County residents, to demonstrate a predictive analytic approach to perinatal infant risk assessment.

The primary aim of this study was to apply a novel technology acceptance model (TAM) for virtual reality (VR) in healthcare. The secondary aim was to assess reliability of this model to evaluate factors that predict the intentions of pediatric health providers' use of VR as an anxiolytic for hospitalized pediatric patients.

Health-related chatbots have demonstrated early promise for improving self-management behaviors but have seldomly been utilized for hypertension. This research focused on the design, development, and usability evaluation of a chatbot for hypertension self-management, called "Medicagent."