Informatik
Refine
Year of publication
- 2022 (31) (remove)
Document Type
- Journal article (31) (remove)
Has full text
- yes (31)
Is part of the Bibliography
- yes (31)
Institute
- Informatik (31)
Publisher
- Springer (8)
- Elsevier (7)
- De Gruyter (4)
- Association of Computing Machinery (1)
- Cornell Universiy (1)
- Deutsche Aktuarvereinigung (DAV) e.V. (1)
- EDP Sciences (1)
- IADIS (1)
- IARIA (1)
- IEEE (1)
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
Public transport maps are typically designed in a way to support route finding tasks for passengers, while they also provide an overview about stations, metro lines, and city-specific attractions. Most of those maps are designed as a static representation, maybe placed in a metro station or printed in a travel guide. In this paper, we describe a dynamic, interactive public transport map visualization enhanced by additional views for the dynamic passenger data on different levels of temporal granularity. Moreover, we also allow extra statistical information in form of density plots, calendar-based visualizations, and line graphs. All this information is linked to the contextual metro map to give a viewer insights into the relations between time points and typical routes taken by the passengers. We also integrated a graph-based view on user-selected routes, a way to interactively compare those routes, an attribute- and property-driven automatic computation of specific routes for one map as well as for all available maps in our repertoire, and finally, also the most important sights in each city are included as extra information to include in a user-selected route. We illustrate the usefulness of our interactive visualization and map navigation system by applying it to the railway system of Hamburg in Germany while also taking into account the extra passenger data. As another indication for the usefulness of the interactively enhanced metro maps we conducted a controlled user experiment with 20 participants.
Adoption of artificial intelligence (AI) has risen sharply in recent years but many firms are not successful in realising the expected benefits or even terminate projects before completion. While there are a number of previous studies that highlight challenges in AI projects, critical factors that lead to project failure are mostly unknown. The aim of this study is therefore to identify distinct factors that are critical for failure of AI projects. To address this, interviews with experts in the field of AI from different industries are conducted and the results are analyzed using qualitative analysis methods. The results show that both, organizational and technological issues can cause project failure. Our study contributes to knowledge by reviewing previously identified challenges in terms of their criticality for project failure based on new empirical data, as well as, by identifying previously unknown factors.
One of the key challenges for automatic assistance is the support of actors in the operating room depending on the status of the procedure. Therefore, context information collected in the operating room is used to gain knowledge about the current situation. In literature, solutions already exist for specific use cases, but it is doubtful to what extent these approaches can be transferred to other conditions. We conducted a comprehensive literature research on existing situation recognition systems for the intraoperative area, covering 274 articles and 95 cross-references published between 2010 and 2019. We contrasted and compared 58 identified approaches based on defined aspects such as used sensor data or application area. In addition, we discussed applicability and transferability. Most of the papers focus on video data for recognizing situations within laparoscopic and cataract surgeries. Not all of the approaches can be used online for real-time recognition. Using different methods, good results with recognition accuracies above 90% could be achieved. Overall, transferability is less addressed. The applicability of approaches to other circumstances seems to be possible to a limited extent. Future research should place a stronger focus on adaptability. The literature review shows differences within existing approaches for situation recognition and outlines research trends. Applicability and transferability to other conditions are less addressed in current work.
Background
Although teledermatology has been proven internationally to be an effective and safe addition to the care of patients in primary care, there are few pilot projects implementing teledermatology in routine outpatient care in Germany. The aim of this cluster randomized controlled trial was to evaluate whether referrals to dermatologists are reduced by implementing a store-and-forward teleconsultation system in general practitioner practices.
Methods
Eight counties were cluster randomized to the intervention and control conditions. During the 1-year intervention period between July 2018 and June 2019, 46 general practitioner practices in the 4 intervention counties implemented a store-and-forward teledermatology system with Patient Data Management System interoperability. It allowed practice teams to initiate teleconsultations for patients with dermatologic complaints. In the four control counties, treatment as usual was performed. As primary outcome, number of referrals was calculated from routine health care data. Poisson regression was used to compare referral rates between the intervention practices and 342 control practices.
Results
The primary analysis revealed no significant difference in referral rates (relative risk = 1.02; 95% confidence interval = 0.911–1.141; p = .74). Secondary analyses accounting for sociodemographic and practice characteristics but omitting county pairing resulted in significant differences of referral rates between intervention practices and control practices. Matched county pair, general practitioner age, patient age, and patient sex distribution in the practices were significantly related to referral rates.
Conclusions
While a store-and-forward teleconsultation system was successfully implemented in the German primary health care setting, the intervention's effect was superimposed by regional factors. Such regional factors should be considered in future teledermatology research.
In our initial DaMoN paper, we set out the goal to revisit the results of “Starring into the Abyss [...] of Concurrency Control with [1000] Cores” (Yu in Proc. VLDB Endow 8: 209-220, 2014). Against their assumption, today we do not see single-socket CPUs with 1000 cores. Instead, multi-socket hardware is prevalent today and in fact offers over 1000 cores. Hence, we evaluated concurrency control (CC) schemes on a real (Intel-based) multi-socket platform. To our surprise, we made interesting findings opposing results of the original analysis that we discussed in our initial DaMoN paper. In this paper, we further broaden our analysis, detailing the effect of hardware and workload characteristics via additional real hardware platforms (IBM Power8 and 9) and the full TPC-C transaction mix. Among others, we identified clear connections between the performance of the CC schemes and hardware characteristics, especially concerning NUMA and CPU cache. Overall, we conclude that no CC scheme can efficiently make use of large multi-socket hardware in a robust manner and suggest several directions on how CC schemes and overall OLTP DBMS should evolve in future.
Hybrid project management is an approach that combines traditional and agile project management techniques. The goal is to benefit from the strengths of each approach, and, at the same time avoid the weaknesses. However, due to the variety of hybrid methodologies that have been presented in the meantime, it is not easy to understand the differences or similarities of the methodologies, as well as, the advantages or disadvantages of the hybrid approach in general. Additionally, there is only fragmented knowledge about prerequisites and success factors for successfully implementing hybrid project management in organizations. Hence, the aim of this study is to provide a structured overview of the current state of research regarding the topic. To address this aim, we have conducted a systematic literature review focusing on a set of specific research questions. As a result, four different hybrid methodologies are discussed, as well as, the definition, benefits, challenges, suitability and prerequisites of hybrid project management. Our study contributes to knowledge by synthesizing and structuring prior work in this growing area of research, which serves as a basis for purposeful and targeted research in the future.
Purpose
Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice.
Methods
In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN.
Conclusion
Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI.
Purpose
Context awareness in the operating room (OR) is important to realize targeted assistance to support actors during surgery. A situation recognition system (SRS) is used to interpret intraoperative events and derive an intraoperative situation from these. To achieve a modular system architecture, it is desirable to de-couple the SRS from other system components. This leads to the need of an interface between such an SRS and context-aware systems (CAS). This work aims to provide an open standardized interface to enable loose coupling of the SRS with varying CAS to allow vendor-independent device orchestrations.
Methods
A requirements analysis investigated limiting factors that currently prevent the integration of CAS in today's ORs. These elicited requirements enabled the selection of a suitable base architecture. We examined how to specify this architecture with the constraints of an interoperability standard. The resulting middleware was integrated into a prototypic SRS and our system for intraoperative support, the OR-Pad, as exemplary CAS for evaluating whether our solution can enable context-aware assistance during simulated orthopedical interventions.
Results
The emerging Service-oriented Device Connectivity (SDC) standard series was selected to specify and implement a middleware for providing the interpreted contextual information while the SRS and CAS are loosely coupled. The results were verified within a proof of concept study using the OR-Pad demonstration scenario. The fulfillment of the CAS’ requirements to act context-aware, conformity to the SDC standard series, and the effort for integrating the middleware in individual systems were evaluated. The semantically unambiguous encoding of contextual information depends on the further standardization process of the SDC nomenclature. The discussion of the validity of these results proved the applicability and transferability of the middleware.
Conclusion
The specified and implemented SDC-based middleware shows the feasibility of loose coupling an SRS with unknown CAS to realize context-aware assistance in the OR.
The paper describes how eye-tracking can be used to explore electronic patient records (EPR) in a sterile environment. As an information display, we used a system that we developed for the presentation of patient data and for supporting surgical hand disinfection. The eye-tracking was performed using the Tobii Eye Tracker 4C, and the connection between the eye-tracker and the HTML website was realized using the Tobii EyeX Chrome Extension. Interactions with the EPR are triggered by fixations of icons. The interaction was working as intended, but test persons reported a high mental load while using the system.