Background Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers’ efforts have led to the emergence of the disaster eHealth (DEH) field. DEH’s main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). Objective This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. Methods An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. Results In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. Conclusions Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.
This paper is concerned with the structural reliability of conjoint measurement when applied in a health care setting. The clinical context was the diagnosis and treatment of knee injuries. A conjoint measurement study was conducted which used the pairwise choice approach to preference elicitation. Each choice included two scenarios: a conventional treatment approach to management (arthroscopy) and an approach using magnetic resonance imaging. In order to test for structural reliability two separate conjoint measurement exercises were conducted: exercise A where scenarios were defined in terms of three attributes and exercise B where scenarios included all four attributes. The assessment of structural reliability involved a comparison of two random effects probit models, for exercises A and B. Data were collected on a total of 176 students of sports science. The results strongly indicate that the models for the two exercises are different, although the instability is limited to the constant term and a single model attribute (i.e. the avoidance of surgery). The finding of instability in the constant coefficient raises important questions about the appropriateness of labelling scenarios in conjoint measurement exercises.
BackgroundKnowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.ObjectiveThe two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.MethodsWe conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.ResultsThe retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.ConclusionsThe potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.
Evidence-based medicine (EBM) requires appropriate information to be available to clinicians at the point of care. Electronic sources of information may fulfill this need but require a high level of skill to use successfully. This paper describes the rationale and initial testing of a system to allow collaborative search and ontology construction for professional groups in the health sector. The approach is based around the use of a browser using a fuzzy ontology based on the National Library of Medicine (NLM) Unified Medical Language System (UMLS). This approach may provide high quality information for professionals in the future.
Objective: This study examined the data modeling capability of spiking neural networks (SNN) in classifying stressed versus relaxed brain states using electroencephalogram (EEG) data. The input spatiotemporal dynamics were explored to obtain further knowledge regarding the two-brain states. Method: A publicly available EEG data set for emotion analysis using psychological signals (DEAP) collected from 32 participants (50% females) with an average age of 26.9 is used in this study. Firstly, data extraction is performed using a criterion that defines stress and relaxation states using self-reported valence and arousal scores. Two hundred eight such extracted samples were selected to train and evaluate a novel three-layer feedforward SNN. This SNN consisted of leaky-integrate and fire neurons and learned from incoming data using spike-time-dependent plasticity (STDP) and dynamically evolving SNN algorithms. The SNN performance was evaluated using both fivefold cross-validation and a 60:40 training testing split. To explore input spatiotemporal dynamics, a specialized SNN architecture for brain data processing named NeuCube was used. Results: The highest-performing model of the novel SNN algorithm produced 88% average accuracy (F1 score: 86.21%, Matthews correlation coefficient: 0.78). This SNN outperformed traditional machine learning (ML) techniques without the use of manual feature extraction. Moreover, the input dynamics revealed higher prefrontal activation during relaxation states compared to stress states. Conclusions: The results showed the capability of the SNN algorithm to recognize stressed and relaxed states of the brain, using temporal learning techniques. Furthermore, the findings obtained from NeuCube suggested a potential approach for brain data analysis, setting SNNs apart from black box approaches used for brain data processing.
Oral poster abstractsexcluded the patients with the vaginal and cervical laceration and the cases of Cesarean section (27 cases), postpartum hysterectomy (5 cases). At each scan we evaluated the location of bleeding points. If we could not find out the bleeding points, at the first, we grasped deeply the uterine cervix with ring forceps in the direction of 12, 3, 6 and 9 o' clock. After that we inserted three tablets of misoprostol (cytotec) to the anus and infused uterotonic drugs (oxytocin). When the bleeding focus was at upper segment of the uterus, we could control the bleeding with uterotonic drugs. If the bleeding was from lower segment of the uterus, we could stop the bleeding with grasping the uterine cervix with ring forceps in the direction of 12, 3, 6 and 9 o' clock and placement of the sutures at the same site. Results: The causes of the bleeding from upper segment of the uterus (15/23, 65%) were placental remnant (9 cases), atonic bleeding (5 cases), uterine inversion (1 case). we could find out the bleeding point in 80% (12/15) on ultrasonographic evaluation and stop bleeding with evacuation, curettage, manual reduction and uterotonic drugs. But we could see the bleeding point in only 25% women (2/8) with bleeding from lower segment of the uterus (8/23, 35%) on ultrasonographic examination. We could control the bleeding in these patients by grasping the cervix with ring forceps in the direction of 12, 3, 6 and 9 o' clock. Conclusions: We think that ultrasonographic examination is useful method to managing the women with postpartum bleeding, particularly the patients with bleeding from upper segment of the uterus. OP12: CONTROVERSIES OP12.01 Evaluation of educational aid to developing countries H. Gilstad NSFM, St Olav, NorwayIntroduction: The Nelson Mandela School of Medicine, Durban, South Africa, and The National Center for Fetal Medicine (NCFM), Trondheim, Norway, have collaborated in developing an educational program in the use of ultrasound technology in pregnancy care for advanced midwives in the province of KwaZulu-Natal, South Africa. The education is taking place on campus in Durban, and a follow-up Internet solution is developed. The educational program is evaluated in a Ph.D. project. The preliminary results are presented here. Objectives: The first objective is to evaluate cross-cultural, communicative and didactic challenges related to the education of the advanced midwives, both when it comes to the education on campus and the education on the Internet. The second objective is to evaluate how the advanced midwives experience the match between the educational program on the one hand and their daily work on the other. Methods: Both quantitative and qualitative methods are applied in the collection of the empirical material. The material is analyzed through theories on language and communication, mainly Critical Discourse Analysis inspired by Norman Fairclough. Results: The evaluations indicate that there is a complexity of aspects influencing the education of the advanced midwives. The ...
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