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Autor: Redakcija 031 Naslov: Potpisan sporazum o suradnji između Akademije za umjetnost i kulturu u Osijeku i Hrvatske komore den
Postano: 12.11.2024. 11:59 (GMT 1) Dana 11. studenog 2024., točno u 11 sati i 11 minuta, u Svečanoj dvorani Akademije za umjetnost i kulturu u Osijeku potpisan je sporazum o suradnji između Akademije i Hrvatske komore dentalne medicine. Sporazum su uime institucija potpisali dekanica Akademije, akademkinja Helena Sablić Tomić, i dr. sc. Hrvoje Pezo, predsjednik Komore.

Sporazum označava početak suradnje između jedinstvene umjetničko-znanstvene institucije u zemlji i strukovne organizacije koja predstavlja dentalnu medicinu u Hrvatskoj. Cilj je sporazuma objediniti znanje i resurse kako bi se postigli viši standardi u razumijevanju i unaprjeđenju zajedničkih područja djelovanja koji se protežu kroz edukaciju, zdravstvo, kulturu i interdisciplinarne projekte koji se razvijaju na specijalističkom studiju Kreativnih terapija pri Akademiji za umjetnost i kulturu u Osijeku.

„Ovim partnerstvom želimo otvoriti nove mogućnosti suradnje u području obrazovanja i profesionalnog razvoja te omogućiti razmjenu iskustava između umjetničko-znanstvene zajednice i medicinske struke“, istaknula je akademkinja Helena Sablić Tomić. „Akademija i Komora mogu se zajedničkim naporima baviti istraživanjem novih pristupa u edukaciji kao i poticanjem šireg društvenog dijaloga o važnosti holističkog pristupa zdravlju.“

Predsjednik Komore dr. sc. Hrvoje Pezo naglasio je da sporazum predstavlja jedinstvenu priliku za razvoj novih standarda u edukaciji o zdravstvenoj kulturi i prevenciji. „Spoj umjetničkog i znanstvenog pristupa, uz medicinsku ekspertizu, omogućuje nam da dublje sagledamo utjecaj koji zdravlje ima na svakodnevni život, ali i na kulturu i društvenu dinamiku. Vjerujemo da će se kroz ovu suradnju razvijati inovativni pristupi obrazovanju i informiranju javnosti,“ izjavio je dr. Pezo.


Tekst i foto: Akademija za umjetnost i kulturu u Osijeku

Provides links to medical study abstracts that find cholesterol control to be an effective means of slowing, stopping or reversing coronary artery disease.

Big Blue Ceiling, a leader in cutting-edge AI and autonomous research technology, is excited to announce AuRA (Autonomous Research Assistant), a groundbreaking AI-powered application set to transform medical research reporting. Built on IntellectCascade, Big Blue Ceiling’s Multiagentic and Multimodal AI framework, AuRA is poised to empower healthcare providers with real-time insights, reshaping patient engagement and [PR.com]

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January 30, 2006 – In his State of the Union Speech tomorrow night, President Bush will propose a new approach to dealing with soaring costs associated with health insurance. But the proposal he will make does little to make insurance more affordable at the consumer level, and does nothing to address the underlying causes for the steep rise in insurance prices. Those who don’t have insurance for affordability reasons, or who can’t get it because for some reason they have been declared uninsurable will find virtually no light at the end of the President’s tunnel.

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Medical science in the 21st century is making strides in areas that once seemed like science fiction. As technology rapidly advances, we’re seeing new possibilities for improving health and longevity. From potential treatments for tooth loss to progress in curing genetic disorders, these innovations aim to address some of the significant health challenges we face […]

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Patient information in health care systems mostly consists of textual data, and free text in particular makes up a significant amount of it. Information retrieval systems that concentrate on these text types have to deal with the different challenges these medical free texts pose to achieve an acceptable performance. This paper describes the evaluation of four different types of information retrieval strategies: keyword search, search performed by a medical domain expert, a semantic based information retrieval tool, and a purely statistical information retrieval method. The different methods are evaluated and compared with respect to its appliance in medical health care systems.

In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application's needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image's security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.

Access to quality healthcare is a fundamental right but unfortunately, India suffers from gender disparities in healthcare access. Telemedicine has the potential to improve access to healthcare services for women by eliminating traditional barriers. Therefore, our research aims to investigate the factors influencing the adoption of telemedicine among Indian women. This study has collected 442 responses and analysed them through structural equation modelling. The result indicates a strong and positive connection between the willingness to adopt telemedicine services and factors like performance expectancy, perceived benefits, e-health literacy, and perceived reliability. Notably, perceived reliability emerges as the most impactful predictor, closely followed by perceived benefits, while factors like effort expectancy and user resistance show no significant influence. This underscores the pivotal role of reliability and perceived benefits in shaping women's inclination toward adopting telemedicine. The study provides practical insights for telemedicine providers and policymakers to customise strategies and policies for effective promotion.

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

Aim/Purpose: Medical images are very sensitive data that can be transferred to medical laboratories, professionals, and specialist for referral cases or consultation. Strict security measures must be utilized to keep these data secured in computer networks when transferred to another party. On a daily basis, unauthorized users derive ways to gain access to sensitive patient medical information. Background: One of the best ways to which medical image could be kept secured is through the use of quantum cryptography Methodology : Applying the principles of quantum mechanics to cryptography has led to a remarkable new dimension in secured network communication infrastructure. This enables two legitimate users to produce a shared secret random bit string, which can be used as a key in cryptographic applications, such as message encryption and authentication. Contribution: This paper can make it possible for the healthcare and medical professions to construct cryptographic communication systems to keep patients’ transferred data safe and secured. Findings: This work has been able to provide a way for two authorized users who are in different locations to securely establish a secret network key and to detect if eavesdropping (a fraudulent or disruption in the network) has occurred Recommendations for Practitioners: This security mechanism is recommended for healthcare providers and practitioners to ensure the privacy of patients’ medical information. Recommendation for Researchers: This paper opens a new chapter in secured medical records Impact on Society Quantum key distribution promises network security based on the fundamental laws of quantum mechanics by solving the problems of secret-key cryptography . Future Research: The use of post-quantum cryptography can be further researched.

Aim/Purpose: This study aims to devise a personalized solution for online healthcare platforms that can alleviate problems arising from information overload and data sparsity by providing personalized healthcare services to patients. The primary focus of this paper is to develop an effective medicine recommendation approach for recommending suitable medications to patients based on their specific medical conditions. Background: With a growing number of people becoming more conscious about their health, there has been a notable increase in the use of online healthcare platforms and e-services as a means of diagnosis. As the internet continues to evolve, these platforms and e-services are expected to play an even more significant role in the future of healthcare. For instance, WebMD and similar platforms offer valuable tools and information to help manage patients’ health, such as searching for medicines based on their medical conditions. Nonetheless, patients often find it arduous and time-consuming to sort through all the available medications to find the ones that match their specific medical conditions. To address this problem, personalized recommender systems have emerged as a practical solution for mitigating the burden of information overload and data sparsity-related issues that are frequently encountered on online healthcare platforms. Methodology: The study utilized a dataset of MC ratings obtained from WebMD, a popular healthcare website. Patients on this website can rate medications based on three criteria, including medication effectiveness, ease of use, and satisfaction, using a scale of 1 to 5. The WebMD MC rating dataset used in this study contains a total of 32,054 ratings provided by 2,136 patients for 845 different medicines. The proposed HSMCCF approach consists of two primary modules: a semantic filtering module and a multi-criteria filtering module. The semantic filtering module is designed to address the issues of data sparsity and new item problems by utilizing a medicine taxonomy that sorts medicines according to medical conditions and makes use of semantic relationships between them. This module identifies the medicines that are most likely to be relevant to patients based on their current medical conditions. The multi-criteria filtering module, on the other hand, enhances the approach’s ability to capture the complexity of patient preferences by considering multiple criteria and preferences through a unique similarity metric that incorporates both distance and structural similarities. This module ensures that patients receive more accurate and personalized medication recommendations. Moreover, a medicine reputation score is employed to ensure that the approach remains effective even when dealing with limited ratings or new items. Overall, the combination of these modules makes the proposed approach more robust and effective in providing personalized medicine recommendations for patients. Contribution: This study addresses the medicine recommendation problem by proposing a novel approach called Hybrid Semantic-based Multi-Criteria Collaborative Filtering (HSMCCF). This approach effectively recommends medications for patients based on their medical conditions and is specifically designed to overcome issues related to data sparsity and new item recommendations that are commonly encountered on online healthcare platforms. The proposed approach addresses data sparsity and new item issues by incorporating a semantic filtering module and a multi-criteria filtering module. The semantic filtering module sorts medicines based on medical conditions and uses semantic relationships to identify relevant ones. The multi-criteria filtering module accurately captures patient preferences and provides precise recommendations using a novel similarity metric. Additionally, a medicine reputation score is also employed to further expand potential neighbors, improving predictive accuracy and coverage, particularly in sparse datasets or new items with few ratings. With the HSMCCF approach, patients can receive more personalized recommendations that are tailored to their unique medical needs and conditions. By leveraging a combination of semantic-based and multi-criteria filtering techniques, the proposed approach can effectively address the challenges associated with medicine recommendations on online healthcare platforms. Findings: The proposed HSMCCF approach demonstrated superior effectiveness compared to benchmark recommendation methods in multi-criteria rating datasets in terms of enhancing both prediction accuracy and coverage while effectively addressing data sparsity and new item challenges. Recommendations for Practitioners: By applying the proposed medicine recommendation approach, practitioners can develop a medicine recommendation system that can be integrated into online healthcare platforms. Patients can then utilize this system to make better-informed decisions regarding the medications that are most suitable for their specific medical conditions. This personalized approach to medication recommendations can ultimately lead to improved patient satisfaction. Recommendation for Researchers: Integrating patient medicine reviews is a promising way for researchers to elevate the proposed medicine recommendation approach. By leveraging patient reviews, the approach can gain a more comprehensive understanding of how certain medications perform for specific medical conditions. Additionally, exploring the relationship between MC-based ratings using an improved aggregation function can potentially enhance the accuracy of medication predictions. This involves analyzing the relationship between different criteria, such as medication effectiveness, ease of use, and satisfaction of the patients, and determining the optimal weighting for each criterion based on patient feedback. A more holistic approach that incorporates patient reviews and an improved aggregation function can enable the proposed medicine recommendation approach to provide more personalized and accurate recommendations to patients. Impact on Society: To mitigate the risk of infection during the COVID-19 pandemic, the promotion of online healthcare services was actively encouraged. This allowed patients to continue accessing care and receiving treatment while adhering to physical distancing guidelines and shielding measures where necessary. As a result, the implementation of personalized healthcare services for patients is expected to be a major disruptive force in healthcare in the coming years. This study proposes a personalized medicine recommendation approach that can effectively address this issue and aid patients in making informed decisions about the medications that are most suitable for their specific medical conditions. Future Research: One way that may enhance the proposed medicine recommendation approach is to incorporate patient medicine reviews. Furthermore, the analysis of MC-based ratings using an improved aggregation function can also potentially enhance the accuracy of medication predictions.

Aim/Purpose: This research utilized a learn-to-rank algorithm to provide medical recommendations to prescribers. The algorithm has been utilized in other domains, such as information retrieval and recommender systems. Background: Ranking the possible medical treatments according to diagnoses of the medical cases is very beneficial for doctors, especially during the coding process. Methodology: We developed two deep learning pointwise learn-to-rank models within one prediction pipeline: one for predicting the top possible active ingredients from disease features, the other for ranking actual medicines codes from diseases and the ingredients features. Contribution: A new learn-to-rank deep learning model has been developed to rank medical procedures based on datasets collected from insurance companies. Findings: We ran 18 cross-validation trials on a confidential dataset from an insurance company. We obtained an average normalized discounted cumulative gain (NDCG@8) of 74% with a 5% standard deviation as a result of all 18 experiments. Our approach outperformed a known approach used in the information retrieval domain in which data is represented in LibSVM format. Then, we ran the same trials using three learn-to-rank models – pointwise, pairwise, and listwise – which yielded average NDCG@8 of 71%, 72%, and 72%, respectively. Recommendations for Practitioners: The proposed model provides an insightful approach to helping to manage the patient’s treatment process. Recommendation for Researchers: This research lays the groundwork for exploring various applications of data science techniques and machine learning algorithms in the medical field. Future studies should focus on the significant potential of learn-to-rank algorithms across different medical domains, including their use in cost-effectiveness models. Emphasizing these algorithms could enhance decision-making processes and optimize resource allocation in healthcare settings. Impact on Society: This will help insurance companies and end users reduce the cost associated with patient treatment. It also helps doctors to choose the best procedure and medicines for their patients. Future Research: Future research is required to investigate the impact of medicine data at a granular level.

Aim/Purpose: These days educators are expected to integrate technological tools into classes. Although they acquire relevant skills, they are often reluctant to use these tools. Background: We incorporated online forums for generating a Community of Inquiry (CoI) in a faculty development program. Extending the Technology, Pedagogy, and Content Knowledge (TPACK) model with Assessment Knowledge and content analysis of forum discourse and reflection after each CoI, we offer the Diagnostic Tool for Learning, Assessment, and Research (DTLAR). Methodology: This study spanned over two cycles of a development program for medical faculty. Contribution: This study demonstrates how the DTLAR supports in-depth examination of the benefits and challenges of using CoIs for learning and teaching. Findings: Before the program, participants had little experience with, and were reluctant to use, CoIs in classes. At the program completion, many were willing to adopt CoIs and appreciated this method’s contribution. Both CoIs discourse and reflections included positive attitudes regarding cognitive and teacher awareness categories. However, negative attitudes regarding affective aspects and time-consuming aspects of CoIs were exposed. Participants who experienced facilitating a CoI gained additional insights into its usefulness. Recommendations for Practitioners : The DTLAR allows analyzing adaption of online forums for learning and teaching. Recommendation for Researchers: The DTLAR allows analyzing factors that affect the acceptance of online fo-rums for learning and teaching. Impact on Society : While the tool was implemented in the context of medical education, it can be readily applied in other adult learning programs. Future Research: The study includes several design aspects that probably affected the improve-ment and challenges we found. Future research is called for providing guidelines for identifying boundary conditions and potential for further improvement.

Certain trends in scientific research have important relevance to bioethics theory and practice. A growing stream of literature relates to increasing transparency and inclusivity of populations (stakeholders) in scientific research, from high volume data collection, synthesis, and analysis to verification and ethical scrutiny. The emergence of this stream of literature has implications for bioethics theory and practice. This paper seeks to make explicit these streams of literature and to relate these to bioethical issues, through consideration of certain extreme examples of scientific research where bioethical engagement is vital. Implications for theory and practice are derived, offering useful insights derived from multidisciplinary theory. Arguably, rapidly developing fields of citizen science such as informing science and others seeking to maximise stakeholder involvement in both research and bioethical engagement have emerged as a response to these types of issues; radically enhanced stakeholder engagement in science may herald a new maximally inclusive and transparent paradigm in bioethics based on lessons gained from exposure to increasingly uncertain ethical contexts of biomedical research.

Labor pain and its relieving methods is one of the anxieties of mothers having a great impact on the quality of care during delivery as well as the patients' satisfaction. The propensity of using non-medicinal pain relief methods is increasing. The present study aimed to compare the effect of Acupressure at two GB-21 and SP06 points on the severity of labor pain. In this quasi-experimental single blind study started on December 2010 and ended on June 2011 in which 150 primiparous women were divided into three groups of Acupressure at GB-21 point, Acupressure at SP-6 point and control group. The intervention was carried out for 20 min at 3-4 and 20 min at 7-8 cm dilatation of Cervix. The pain severity was measured by Visual Analog Scale before and immediately, 30 and 60 min after the intervention. Then, the data were statistically analyzed. No significant difference was found among the 3 groups regarding the pain severity before the intervention. However, the pain severity it was reduced at 3-4 and 7-8 cm dilatation immediately, 30 and 60 min after the intervention in the two intervention groups compared to the control group (p<0.001). Nonetheless, no statistically significant difference was observed between the two intervention groups (p = 0.93). The results of the study showed that application of Acupressure at two GB-21 and SP-6 points was effective in the reduction of the severity of labor pain. Therefore, further studies are recommended to be performed on the application of Acupressure together with non-medicinal methods.

BATU KAWAN: US-based medical devices company Dexcom Inc has officially opened its manufacturing facility, also its first offshore manufacturing site outside the United States, in Batu Kawan, Penang.

Penang Chief Minister Chow Kon Yeow said the RM2.83 billion strategic investment will bring more than 3,000 jobs to the state, contributing to a workforce set to positively impact the lives of over three million people worldwide.

Dexcom, founded in 1999, is a global leader in continuous glucose Monitoring (CGM) technology for individuals living with diabetes.

“The establishment of this new facility highlights Dexcom’s continued commitment to take control of health through innovative CGM systems. It also reaffirms Penang’s reputation as a global hub for advanced technological industries, reinforcing its position as a preferred destination for high-quality manufacturing and innovation,” the chief minister said in his speech at the opening ceremony here today.

Chow said Penang is on the right path towards becoming the medical technology (medtech) hub of Southeast Asia by leveraging on the state’s over 50 years of industry excellence.

“Housing the largest number of medtech companies nationally and regionally, Penang remains a highly attractive location for its infrastructure availability and ecosystem that meet the needs of the medtech industry.

“For the past five years (2019-2023), Penang garnered a total of RM5.8 billion worth of investments in the scientific and measuring equipment sector, representing 45% of the nation’s total investments in this sector, involving 33 projects and generating an estimated 4,630 employment opportunities,” he said.

Dubbed the Silicon Valley of the East, Penang has the highest concentration of medical technology companies in Malaysia and Southeast Asia to date. – Bernama

KUALA LUMPUR: As the takaful industry contends with escalating medical inflation and the rising costs of healthcare, the Malaysian Takaful Association (MTA) has called for increased government and private sector collaboration to ensure affordable, accessible protection options for all Malaysians.

MTA CEO Mohd Radzuan Mohamed envisions takaful becoming a leading option for Malaysians seeking protection.

With only 60% of the population currently insured or covered by takaful, he sees untapped potential among the remaining 40%, primarily in underserved and unserved communities.

“Our goal is to make takaful an accessible option, particularly for groups like the BM40 (top 20% of B40 and bottom 40% M40), who often do not receive enough attention from traditional agents,” he told SunBiz at the recent launch of Hijrah27.

Radzuan said the takaful sector has struggled to expand its market share, currently at about 20% after four decades.

In response, he said, they are considering how digital transformation, distribution enhancements and technology can address these challenges.

“Malaysia’s transformation plan and recent initiatives, like the financial sector blueprint, outline steps to close this gap by focusing on underserved communities, leveraging financial literacy campaigns, and engaging technology to make takaful accessible,” he added.

Additionally, he said co-payments, a shared cost model between the insurer and the insured, is seen as a potential solution to rising premiums fuelled by medical inflation.

“Collaborating with the Ministry of Health and Bank Negara Malaysia, takaful providers aim to develop cost-sharing models that help balance affordability and coverage. With co-payments, premiums could become more manageable, helping to mitigate the effects of medical inflation and making Takaful accessible for more people.”
While the adoption of co-payments is currently optional, Radzuan said, making it mandatory could have far-reaching implications on the takaful industry. “A balanced co-payment structure can be beneficial, but affordability must remain a priority to avoid defeating the purpose of protection,” he emphasised.

Looking forward, Radzuan said the association is exploring new technology-driven initiatives, such as the Hijrah27 framework, to improve customer service and operational efficiency. “Collaborations with fintech firms and the rise of AI-based solutions also show promise in elevating the industry’s standards and expanding reach,” he added.

With more than 67 million beneficiaries, around one in five Americans is enrolled in Medicare. The federal health insurance program has helped ensure adults over 65 and individuals with disabilities have access to health insurance since 1965. But with regular changes to the program — and our health system at large — it’s no wonder […]

The post Medicare: 10 Things To Know appeared first on Clark Howard.

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Say they want ‘hard facts’ on the matter.