Enhancing Arden-Syntax-Based Clinical Reasoning with Ontologies.

We present a new methodological approach based on integrating Arden-Syntax-based clinical decision support (CDS) with an upstream ontology service. Incoming linguistic patient data, such as single reports about detected germs or viruses, shall be identified by the applied ontology at a low level. Then, higher-level concepts are activated by ontology-based bottom-up reasoning.

Microbiological and Virological Knowledge-Based Alert Service.

Hospital wards need immediate information about multi-resistant pathogens and contagious viruses in their hospitalized patients. An alert service configurable with Arden-Syntax-based alert definitions passing through an ontology service to complement results from microbiology and virology with high-level terms was implemented as proof of concept. Integration into the University Hospital Vienna's IT landscape is ongoing.

Septoplasties are unlikely to cause postoperative numbness of the anterior palate.

Background: Septoplasties and septorhinoplasties are very commonly performed surgical procedures in modern aesthetic and functional medicine. Throughout the surgery, close manipulation to the incisive nerves' course is being executed. This retrospective analysis followed up on potential sensitivity disorders of the anterior palate due to nerve damage.

COMP Report: A survey of radiation safety regulations for medical imaging x-ray equipment in Canada.

X-ray regulations and room design methodology vary widely across Canada. The Canadian Organization of Medical Physicists (COMP) conducted a survey in 2016/2017 to provide a useful snapshot of existing variations in rules and methodologies for human patient medical imaging facilities. Some jurisdictions no longer have radiation safety regulatory requirements and COMP is concerned that lack of regulatory oversight might erode safe practices.

Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice.

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased availability for patients who urgently need it. Common risk assessment tool, like the ASA score, lack objectivity and do account only for some influencing parameters. The aim of our study was (1) to develop a reliable machine learning model predicting ICU admission risk after elective surgery, and (2) to implement it in a clinical workflow.