Studies in health technology and informatics
27139386
Standard-based integration and semantic enrichment of clinical data originating from electronic medical records has shown to be critical to enable secondary use. To facilitate the utilization of semantic technologies on clinical data, we introduce a ...
BACKGROUND: Since early antimicrobial therapy is mandatory in septic patients, immediate diagnosis and distinction from non-infectious SIRS is essential but hampered by the similarity of symptoms between both entities. We aimed to develop a diagnosti...
IEEE journal of biomedical and health informatics
30676988
This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze ...
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...
Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are mul...
PURPOSE: Robot-assisted kidney transplant (RAKT) recently proved to provide functional results similar to the preferred open kidney transplant (OKT), but with inferior wound morbidity. In a comparative prospective study, we explored the systemic infl...
BACKGROUND: Robot-assisted surgery is being increasingly adopted in treating colorectal cancer, and the transition from laparoscopic surgery to robot-assisted surgery is a trend. The evidence of the benefits of robot-assisted surgery is sparse. Howev...
Studies in health technology and informatics
35062134
Critical care can benefit from analyzing data by machine learning approaches for supporting clinical routine and guiding clinical decision-making. Developing data-driven approaches for an early detection of systemic inflammatory response syndrome (SI...
OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).