Medical guidelines have a significant role in the field of evidence-based medical treatment. The content of a medical guideline is based on a systematic review of clinical evidence with instructions and recommendations that clinicians can refer to. Most of the guidelines are available in an unstructured text format. Hence, clinicians must take a considerable time to search and find relevant recommendations in their semantic context. Using Machine Learning algorithms, automatic information extraction from medical guidelines has recently become possible. We present a novel system for information extraction and a fuzzy rule database developed for clinical guidelines.
1031 – 1035
Date of Publication:
17 May 2019
In this work, a new hybrid algorithm for disease risk classification is proposed. The proposed methodology is based on Dynamic Time Warping (DTW). This methodology can be applied to time series from various domains such as vital sign time series available in medical big data. To validate our methodology, we applied it to risk classification for sepsis, which is one of the most challenging problems within the area of medical data analysis. In the first step the algorithm uses different statistical properties of time series features. Furthermore, using differently labeled training data sets, we created a DTW Barycenter Averaging (DBA) on each feature.
In the second step, validation data sets and DTW are used to validate the precision of classification and the final results are compared. The performance of our methodology is validated with real medical data and on six different criteria definitions for the sepsis diseases. Results show that our algorithm performed, in the best case, with precision and recall of 96,38% and 90,90%, respectively.
Lejla Begic Fazlic, Ahmed Hallawa, Matthias Dziubany, Marlies Morgen, Jens Schneider, Marvin Schacht, Anke Schmeink, Lukas Martin, Arne Peine, Thomas Vollmer, Stefan Winter and Guido Dartmann, 8th International Conference on Cyber – Physical Systems and Internet-of-Things (CPS & IoT‘ 2020), Budva, Montenegro, 2020.
Date of Conference:
8-11 June 2020
Decision support systems in intensive care units are developed with safety, efficiency, and effectiveness in mind. In contrast, user experience (UX) for decision support systems has received limited attention in practice and research. In this paper, we present an application of the Experience Design approach from Hassenzahl  for a clinical decision support system (CDSS) for volume therapy – the administration of intravenous fluids – in intensive care units. In semi-structured interviews with five nurses, we gathered nurses‘ work activities around volume therapy, which are perceived as particularly beneficial for their well-being. Lead by the narratives gathered in the interviews we designed positive experience through the CDSS. The resulting concept of a CDSS for nurses aims to create a positive experience during volume therapy, which is supported by addressing and fulfilling the needs for competence and popularity.