Electronic Health Records-based identification of newly diagnosed Crohn's Disease cases.

Background: Early diagnosis and treatment of Crohn's Disease are associated with decreased risk of surgery and complications. However, diagnostic delay is frequently seen in clinical practice. To better understand Crohn's Disease risk factors and disease indicators, we identified, described, and predicted incident Crohn's Disease patients based on the Electronic Health Record data of the Mount Sinai Health System.

COVID-19 in German Competitive Sports: Protocol for a Prospective Multicenter Cohort Study (CoSmo-S).

It is unclear whether and to what extent COVID-19 infection poses health risks and a chronic impairment of performance in athletes. Identification of individual health risk is an important decision-making basis for managing the pandemic risk of infection with SARS-CoV-2 in sports and return to play (RTP). This study aims 1) to analyze the longitudinal rate of seroprevalence of SARS-CoV-2 in German athletes, 2) to assess health-related consequences in athletes infected with SARS-CoV-2, and 3) to reveal effects of the COVID-19 pandemic in general and of a cleared SARS-CoV-2 infection on exercise performance.

FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling.

Objectives: The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries.

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review.

Background: A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain.

Objective: This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements, using sensors and wearables such as inertial measurement units, pressure or piezoresistive sensors, accelerometers or gyroscopes, combined with machine learning approaches.

Dependence of treatment planning accuracy in peptide receptor radionuclide therapy on the sampling schedule.

Background: Peptide receptor radionuclide therapy (PRRT) plays an important role in the treatment of neuroendocrine tumors (NET). Pre-therapeutic dosimetry using the area under the measured time-activity curve (AUC) is important. The sampling schedule for this dosimetry determines the accuracy and reliability of the obtained AUC.