Heart Disease Prediction using Machine Learning Techniques

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

Medical 3D Graphics With eXtensible 3D

As the healthcare enterprise is adopting novel imaging and health-assessment technologies, we are facing unprecedented requirements in information sharing, patient empowerment, and care coordination within the system. Medical experts not only within US, but around the world should be empowered through collaboration capabilities on 3D data to enable solutions for complex medical problems that will save lives. The fast-growing number of 3D medical ‘images' and their derivative information must be shared across the healthcare enterprise among stakeholders with vastly different perspectives and different needs. The demand for 3D data visualization is driving the need for increased accessibility and sharing of 3D medical image presentations, including their annotations and their animations. As patients have to make decisions about their health, empowering them with the right tools to understand a medical procedure is essential both in the decision-making process and for knowledge sharing.

Opioid prescription patterns among patients with fibromyalgia

Objectives: To investigate opioid prescribing patterns among patients with fibromyalgia (FM) in terms of age, gender, race, type of opioids, and to examine changes in opioid prescription over the past 8 years compared to the US Food and Drug Administration (FDA)-approved FM medications.Design: Retrospective review of data using the Healthcare Enterprise Repository for Ontological Narration database. The collected data were analyzed descriptively and a chi-square test for trend was used to analyze a possible linear relationship between the proportions of opioid and non-opioid users along the time.Participants: Patients with a diagnosis of FM who had received opioid prescriptions from January 1, 2010 to December 31, 2017, and FM patients who had received prescriptions of FDA-approved FM medications in the same period. Main outcome measure: Trends in opioid and non-opioid prescriptions in patients with FM.Results: The opioid medications were prescribed more frequently in 2010 (40 percent) and 2011 (42 percent), but the percentages have decreased since 2012 and reached the lowest numbers in 2016 (27 percent). The chi-square test for trend shows that from 2010 to 2017 the prescriptions of opioids had a statistically significant (p 0.0001) decrease.Conclusion: This study suggests that the frequency of prescribed opioids in FM patients has decreased since 2012. This decline could be attributed to (1) FDA monitoring programs, (2) national efforts to increase awareness of the addictive and harmful effects of opioids, and (3) the growing research on the efficacy of non-opioid therapies to treat chronic pain conditions including FM.

Sustainable Social Enterprise Model: Relationships and Consequences

The present study develops a sustainable social enterprise model and examines relationships between corporate sustainability practices and sustainability performance outputs in a social healthcare enterprise in Thailand. Findings reveal four predictors of corporate sustainability, including Leadership, Stakeholder Focus, Resilience Development, and Sharing practices. All of them have direct and/or indirect positive effects on corporate sustainability performance outputs as measured by brand equity, socioeconomic performance and environmental performance. The study also proposes a coherent theory of Sufficiency Economy in business, build upon key theories from relevant fields. Lastly, the present study provides future research directions and managerial implications based upon the model.

Enabling Technologies

Enabling technologies are products and processes that complement more established techniques based on the electronic health record by broadening and easing the process of data collection. Often, these technologies are used to provide more effective means of communication among patients and providers, collect data from home and body sensors, and store data in more accessible ways, all seeking to improve the process or performance of our healthcare system. These include a host of devices and processes that connect providers, connect patients to providers, and add data outside the healthcare enterprise to the personal health record. This has the promise of further improving healthcare.