Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic.

Early Post-Hospitalization Hemoglobin Recovery and Clinical Outcomes in Survivors of Critical Illness: A Population-Based Cohort Study.

Anemia is common during critical illness, is associated with adverse clinical outcomes, and often persists after hospitalization. The goal of this investigation is to assess the relationships between post-hospitalization hemoglobin recovery and clinical outcomes after survival of critical illness. This is a population-based observational study of adults (≥18 years) surviving hospitalization for critical illness between January 1, 2010 and December 31, 2016 in Olmsted County, Minnesota, United States with hemoglobin concentrations and clinical outcomes assessed through one-year post-hospitalization.

Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics.

Obesity exerts adverse effects on breast cancer survival, but the means have not been fully elucidated. We evaluated obesity as a contributor to breast cancer survival according to tumor molecular subtypes in a population-based case-cohort study using data from the Surveillance Epidemiology and End Results (SEER) program. We determined whether obese women were more likely to be diagnosed with poor prognosis tumor characteristics and quantified the contribution of obesity to survival.

Carbon Capture Simulation Initiative: a case study in multiscale modeling and new challenges.

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process.