2,311 research outputs found

    Drug prescription support in dental clinics through drug corpus mining

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    The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

    Drug prescription support in dental clinics through drug corpus mining

    Get PDF
    The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

    Decision support systems for adoption in dental clinics: a survey

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    While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic

    Mining drug properties for decision support in dental clinics

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    The rise of polypharmacy requires from health providers an awareness of a patient’s drug profile before prescribing. Existing methods to extract information on drug interactions do not integrate with the patient’s medical history. This paper describes state-of-the-art approaches in extracting the term frequencies of drug properties and combining this knowledge with consideration of the patient’s drug allergies and current medications to decide if a drug is suitable for prescription. Experimental evaluation of our models association of the similarity ratio between two drugs (based on each drug’s term frequencies) with the similarity between them yields a superior accuracy of 79%. Similarity to a drug the patient is allergic to or is currently taking are important considerations as to the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical workflow, will reduce prescription errors thereby increasing the health outcome of the patient

    General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine

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    This report is the collective product of word-leading experts working in the branches of integrative medicine by predictive, preventive and personalised medicine (PPPM) under the coordination of the European Association for Predictive, Preventive and Personalised Medicine. The general report has been prepared as the consortium document proposed at the EPMA World Congress 2011 which took place in Bonn, Germany. This forum analyzed the overall deficits and trends relevant for the top-science and daily practice in PPPM focused on the patient. Follow-up consultations resulted in a package of recommendations for consideration by research units, educators, healthcare industry, policy-makers, and funding bodies to cover the current knowledge deficit in the field and to introduce integrative approaches for advanced diagnostics, targeted prevention, treatments tailored to the person and cost-effective healthcare

    Integrating social and health services for people, communities, homes, and places

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    The challenge of an ageing population requires a paradigmatic shift in the way we provide social and healthcare services, demanding the need to prioritize the functionality and independence of older adults. The risk and subsequent fear of falling is one of the most high-risk states for older adults, as it generates a destabilizing effect on their health that is often hard to recover. It is essential to thoroughly address their risk factors and mitigators. This discussion needs to be made in light of a person-centered perspective that goes beyond fragilities to capitalize on the strengths of the older adults. The chapter provides examples of how to connect assessment, interventions, and monitoring to a coherent framework approach that mitigates the risks and the impact of falls on an ageing society. The authors explore how technological innovation, urban planning, and regional policies that are culturally relevant can be incorporated in creating a circular economy while meeting the needs of an aging population and preventing falls and cognitive decline

    Business Plan: dental monitoring

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    There is huge potential for improvement of the healthcare industry through the use of emerging technologies. The problem, however, is, that due to heavy regulation there are just few technology- based solutions that are utilised and the dental market particularly is not yet digitalised. As a basis for the business model, the current state of the art of technology and the adaptiveness capacity in healthcare was analysed. The outcome demonstrates, that there is an immense need for patient- centred solutions and that digital transformation in healthcare often fails due to the complex implication. Furthermore, trends and challenges have been analysed, which reveal that the main challenge is a lack of societal acceptance for current data protection regulations. The main trends are Artificial Intelligence (AI) and wearable medical devices. The internal analysis comes up with strategy propositions to turn the market potential into an actual income stream, and design the service in a way, that is easy to use and avoids the challenges outlined above. Finally, the income statement underpins the business´ potential in the market with numbers, based on assumptions and findings. To get key insights and prove the businesses relevance, the business model was presented to 20 dental market experts, who filled out a survey to gather data and draw conclusions from it. The primary research uncovered that the trends detected are relevant and the market need for such a solution is identified. Generally, the results are in line with the business model´s solution and its expectations.There is huge potential for improvement of the healthcare industry through the use of emerging technologies. The problem, however, is, that due to heavy regulation there are just few technology- based solutions that are utilised and the dental market particularly is not yet digitalised. As a basis for the business model, the current state of the art of technology and the adaptiveness capacity in healthcare was analysed. The outcome demonstrates, that there is an immense need for patient- centred solutions and that digital transformation in healthcare often fails due to the complex implication. Furthermore, trends and challenges have been analysed, which reveal that the main challenge is a lack of societal acceptance for current data protection regulations. The main trends are Artificial Intelligence (AI) and wearable medical devices. The internal analysis comes up with strategy propositions to turn the market potential into an actual income stream, and design the service in a way, that is easy to use and avoids the challenges outlined above. Finally, the income statement underpins the business´ potential in the market with numbers, based on assumptions and findings. To get key insights and prove the businesses relevance, the business model was presented to 20 dental market experts, who filled out a survey to gather data and draw conclusions from it. The primary research uncovered that the trends detected are relevant and the market need for such a solution is identified. Generally, the results are in line with the business model´s solution and its expectations

    National eHealth system – platform for preventive, predictive and personalized diabetes care

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    National eHealth System, covering all citizens and all healthcare levels in Republic of Macedonia, was introduced in July 2013, has been internationally recognized System for successful reduction of waiting times and instrumental in the management of national healthcare resources. For the first time, National Diabetes Committee, formed in February 2015 according to the Law on healthcare and being overall responsible for the diabetes care in the country, was able to derive exact figures on the national diabetes prevalence from the System, instead of extrapolations used before, serving as a basis for development of strategies for prediction and prevention of diabetic complications, as well as for personalized diabetes care. Number of diabetes cases identified through the National eHealth System in June 2015 was 84,568 (4.02 % of total population), 36,119 males (3.42 % of total male population) and 48,449 females (4.61% of total female population). Age stratified diabetes prevalence was as follows: less than 20 years – 549 cases (0.11 % of respective population), 20-39 years – 3,202 (0.49 %), 40-59 years – 26,561 (4.58 %), 60-79 years – 48,470 (14.57 %), 80 years or more – 5,786 (12.96 %). Addition of parameters for metabolic control and diabetic complications in the System is under way, further facilitating the modeling of diabetes treatment, metabolic control and the outcomes. Inclusion of pre-diabetes patients (IGT and IFG) is also planned, thus providing opportunity to also focus healthcare activities for prevention of progression into overt type 2 diabetes
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