Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures

2016 ◽  
pp. 271-291
Author(s):  
Stijn Luca Luca ◽  
Lode Vuegen Vuegen ◽  
Hugo Van hamme Van hamme ◽  
Peter Karsmakers Karsmakers ◽  
Bart Vanrumste Vanrumste
Keyword(s):  
Decision Support ◽  
Activities Of Daily Living ◽  
Decision Support Systems ◽  
Daily Living ◽  
Support Systems ◽  
Home Monitoring ◽  
Epileptic Seizures ◽  
Monitoring Applications

Critical Issues in Mobile Solution-Based Clinical Decision Support Systems

2021 ◽  
pp. 32-45
Author(s):  
Nalika Ulapane ◽  
Nilmini Wickramasinghe
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Digital Health ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Primary Objective ◽  
Critical Issues

The use of mobile solutions for clinical decision support is still a rather nascent area within digital health. Shedding light on this important application of mobile technology, this chapter presents the initial findings of a scoping review. The review's primary objective is to identify the state of the art of mobile solution based clinical decision support systems and the persisting critical issues. The authors contribute by classifying identified critical issues into two matrices. Firstly, the issues are classified according to a matrix the authors developed, to be indicative of the stage (or timing) at which the issues occur along the timeline of mobile solution development. This classification includes the three classes: issues persisting at the (1) stage of developing mobile solutions, (2) stage of evaluating developed solutions, and (3) stage of adoption of developed solutions. Secondly, the authors present a classification of the same issues according to a standard socio-technical matrix containing the three classes: (1) technological, (2) process, and (3) people issues.

scholarly journals Decision support systems in Slovak forestry planning: a review / Systémy na podporu rozhodovania v lesníckom plánovaní na Slovensku: prehľad

2015 ◽  
Vol 61 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Ján Tuček ◽  
Róbert Sedmák ◽  
Andrea Majlingová ◽  
Maroš Sedliak ◽  
Susete Marques
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support Systems ◽  
Support Systems ◽  
Computer Based ◽  
Management Techniques ◽  
Planning Problems ◽  
Support Decision Making ◽  
Support Research

Abstract Project COST Action FP 0804 - FORSYS summarizes European experiences in developing and applying decision support systems for forest management. This paper introduces FORSYS methodology for the classification of current forest management problems and for the description of existing decision support systems. The paper identifies the general forestry planning problems that need to be solved in Slovakia, lists the DSS tools available in Slovakia and evaluate their ability for addressing the identified problems. Finally, the research needs and gaps in this field were identified. A comparison of the situation regarding decision support in Slovakia and both in Europe and neighbouring countries (Austria, Hungary) is introduced in order to justify the identified needs. The paper is focused on the overview of models, methods and knowledge management techniques which are available in Slovakia now. We found out that the Slovak decision support research follows the state in Europe with a significant time delay and a lack of adequate instruments for addressing the contemporary planning problems exists. Consequently, there is a strong need for the development and application of computer-based tools to support decision-making problems in forest management.

Accuracy of aerial sketch-mapping estimates of spruce budworm defoliation in New Brunswick

1996 ◽  
Vol 26 (12) ◽  
pp. 2099-2108 ◽  
Author(s):  
David A. MacLean ◽  
Wayne E. MacKinnon
Keyword(s):  
Decision Support ◽  
New Brunswick ◽  
Decision Support Systems ◽  
Tree Mortality ◽  
Support Systems ◽  
Spruce Budworm ◽  
Weather Conditions ◽  
Balsam Fir ◽  
Small Aircraft

The accuracy of aerial sketch-mapping estimates of spruce budworm (Choristoneurafumiferana (Clem.)) defoliation was evaluated from 1984 to 1993 in 222–325 sample plots in spruce (Picea sp.)–balsam fir (Abiesbalsamea (L.) Mill.) stands in New Brunswick. Operational aerial defoliation estimates were used, wherein all productive forest in known budworm infestation zones was surveyed each year from small aircraft with flight lines 2–5 km apart, and rated in classes of nil (0–10%), light (11–30%), moderate (31–70%), and severe (71–100%). Aerial defoliation estimates were compared with ground-based binocular estimates of current defoliation for an average of 10 trees/plot (range 5–20). Overall, 56% of plots were correctly rated by aerial sketch mapping in four classes (nil, light, moderate, and severe), with 37% of the plots underestimated and 7% overestimated. The predominant error (26% of plots) was rating defoliation as nil (0–10%) from the air when it was actually light (11–30%). This error was deemed not important in terms of predicting tree response, since data from the literature indicated that defoliation less than 30% did not cause tree mortality, although if continued, it would reduce growth. Using three defoliation classes (by combining nil and light, 0–30%), 82% of the plots were correctly classified by aerial sketch mapping. The probability of correct aerial classification of defoliation was significantly affected by defoliation class, weather conditions prior to and during observation flights, and the defoliation class × weather interaction. It was concluded that aerial sketch mapping of spruce budworm defoliation is a viable technique that can be used for both surveys and decision support systems that estimate forest response to budworm outbreaks and management activities.

scholarly journals Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

2021 ◽  
Vol 4 ◽  
Author(s):  
M. E. O’Sullivan ◽  
E. C. Considine ◽  
M. O'Riordan ◽  
W. P. Marnane ◽  
J. M. Rennie ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Fetal Distress ◽  
Well Being ◽  
Fetal Heart Rate Monitoring ◽  
Non Invasive ◽  
Randomised Control Trials ◽  
Selection Of

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation.Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made.Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

scholarly journals INFORMATION TECHNOLOGIES IN DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 133 (5) ◽  
pp. 18-26
Author(s):  
Valentyna MAKOIEDOVA
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Technologies ◽  
Support Systems ◽  
Important Trend ◽  
Cloud Technologies

The article considers classification of decision support systems by the way of inter­action with the user, by the method of support, by the level of data processed by the system. Types of DSS architecture are presented. Intelligent DSS are analyzed. The main areas of research in the field of artificial intelligence are identified. The advantages and disad­vantages of using neural networks are revealed. Cloud technologies are highlighted as an important trend in the development of modern DSS. The properties of Big Data techno­logy are determined.

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