A5037247008- Machine Learning in Healthcare
- Blood transfusion and management
- Palliative Care and End-of-Life Issues
- Hemoglobinopathies and Related Disorders
- Cancer survivorship and care
- Electronic Health Records Systems
- COVID-19 epidemiological studies
- Soil Geostatistics and Mapping
- Patient Dignity and Privacy
- COVID-19 Pandemic Impacts
- Simulation Techniques and Applications
- SARS-CoV-2 and COVID-19 Research
- Sepsis Diagnosis and Treatment
- Data Analysis with R
- Vaccine Coverage and Hesitancy
- Erythropoietin and Anemia Treatment
- Childhood Cancer Survivors' Quality of Life
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Biomedical Text Mining and Ontologies
- Artificial Intelligence in Healthcare
- COVID-19 impact on air quality
- Iron Metabolism and Disorders
- Time Series Analysis and Forecasting
Mayo Clinic in Arizona
2022-2023
Mayo Clinic in Florida
2021-2022
Abstract Objective Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, who could benefit PC do not receive it early enough at all. We sought address this problem by building a predictive model into comprehensive clinical framework the aims (i) identify in-hospital likely consult, and (ii) intervene on such contacting their team. Materials Methods Electronic health record data 68 349 inpatient encounters in...
Anemia is common during critical illness, associated with adverse clinical outcomes, and often persists after hospitalization. The goal of this investigation to assess the relationships between post-hospitalization hemoglobin recovery outcomes survival illness. This a population-based observational study adults (≥18 years) surviving hospitalization for illness January 1, 2010 December 31, 2016 in Olmsted County, Minnesota, United States concentrations assessed through one-year...
Abstract Background As primary care populations age, timely identification of palliative need is becoming increasingly relevant. Previous studies have targeted particular patient with life-limiting disease, but few focused on patients in a setting. Toward this end, we propose stepped-wedge pragmatic randomized trial whereby machine learning algorithm identifies empaneled to units at Mayo Clinic (Rochester, Minnesota, United States) high likelihood need. Methods 42 team 9 clusters were 7...
Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In United States, health care systems governmental agencies relied on several models, such as Institute for Health Metrics Evaluation, Youyang Gu (YYG), Massachusetts of Technology, Centers Disease Control Prevention ensemble, predict short- long-term trends disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed practice...
Anemia is common in patients post-ICU discharge. However, which will develop or recover from anemia remains unclear. Prediction of this population complicated by hospital readmissions, can have substantial impacts on hemoglobin levels due to surgery, blood transfusions, being a proxy for severe illness. We therefore introduce novel Bayesian joint longitudinal model over time, includes specific parametric effects admission and These themselves depend patient's at time hospitalization; given...
Patients are known to be at an elevated risk of severe hemoglobin deficiency (anemia) following Intensive Care Unit (ICU) admission. By using a patient's full history, we create novel functional predictor levels for the year discharge. Because readmission hospital is associated with rapid changes in due surgeries and blood transfusions, incorporate multistate joint longitudinal model so that patients can change between hospitalized home states throughout period interest. This allows state...
Most COVID-19 predictive modeling efforts use statistical or mathematical models to predict national- and state-level cases deaths in the future. These approaches assume parameters such as reproduction time, test positivity rate, hospitalization social intervention effectiveness (masking, distancing, mobility) are constant. However, one certainty with pandemic is that these change over well vary across counties states. In fact, rate of spread region, hospital length stay mortality proportion...