The Web platform for diabetes prediction using weighted machine learning techniques based on personal and clinical indicators

Risk perception of COVID-19 among Portuguese healthcare professionals and the general population

American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) and American Dietetic Association (ADA): Standards of Practice and Standards of Professional Performance for Registered Dietitians (Generalist, Specialty, and Advanced) in Nutrition Support

Color lines: Disparities in pharmacy treatment, education, and practice

Addressing Antibiotic Overuse in the Outpatient Setting: Lessons From Behavioral Economics

Travel Medicine and Transplant Tourism in Solid Organ Transplantation

Person-Centered Kidney Education: The Path Forward

Vaccine hesitancy and unfalsifiability

Artificial intelligence and sexual health in the USA

COPD Surveillance—United States, 1999-2011

Commentary: COVID-19 in patients with diabetes

The Importance of Vaccinations

Quality indicators are classified into system or clinical quality indicators. Typically, different levels of an organization steer each of the two types of indicators. Decentralized levels control clinical indicators (blood pressure, blood sugar etc.) while centralized levels control system indicators (waiting time, electronic health records etc.). In this paper we examine optimal pay-for-performance schemes for the two indicators by considering a model consisting of hierarchy of principal-agent interactions where pay-for-performance rewards are distributed to the centralized level (unit of accountability). We find that the optimal pay-for-performance price depends on factors such as the degree and distribution of altruistic preferences, quality costs, the marginal cost of public funds, and the interdependence between the quality variables. The optimal price should differ for system and clinical indicators both when an internal incentive system is in place and when this is not the case. The optimal price for clinical indicators is to reflect the centralized levels’ ability to steer the decentralized level - the type of internal contract that exists between the two levels of the organization. The optimal price for system indicators is independent of the type of internal contract since such indicators are under the control of the unit of accountability. Finally, it is shown that rewarding organizations on the basis of clinical quality indicators can be optimal also when such incentives are not transmitted to the decentralized level of the organization. This conclusion is the result of the indirect effects that non-incentivized variables (system indicators) might have on the incentivized ones (clinical indicators).Published: Online May 2019.

Ethics of AI

Introduction: Diabetes mellitus is an enduring condition characterized by raised blood glucose levels and has become a significant global health concern. Early and accurate diagnosis is crucial and that can help to prevent or delay of problems like cardiovascular diseases, kidney complications, nerve impairment, and vision diminishing due to diabetes. Diabetes is a chronic metabolic disorder that affects millions of people worldwide, leading to severe health complications if not diagnosed and managed in its early stages. Early prediction of diabetes is crucial for timely intervention and personalized treatment, reducing the risk of long-term complications. Traditional diagnostic approaches rely on clinical tests, which may not always be efficient in identifying high-risk individuals before the onset of the disease. With advancements in artificial intelligence (AI) and machine learning (ML), predictive models have gained prominence in healthcare applications, offering improved accuracy and efficiency in disease diagnosis. However, the performance of these models heavily depends on the quality of input features, data preprocessing techniques, and hyperparameter tuning strategies. Objectives: The main objective of this Research work is to predict diabetes at an early stage so that any severe complications may avoid. Methods: The most significant and robust features of the dataset are chosen using the attribute selection tool and correlation attribute estimation method by using the WEKA software tool. Then, features form the dataset are scaled using the standardization feature scaling technique and different ML classification algorithms such as LR, KNN, Naïve Bayes, Support Vector Machine, Decision Tree and Random Forest are used for experimenting with the above machine algorithm on the PIMA Indian diabetes dataset in PythonIn the preprocessing method, identification and removal of null and duplicate values have been replaced with the mean values. Results: By applying six different machine learning algorithms on PIMA Indian diabetes Dataset have shown that the K-Nearest Neighbor and Naïve Bayes both classifiers reported the maximum prediction accuracy of 81.82%, followed by LR, SVM, and RF with accuracies of 79.87%, 79.22%, and 77.27% respectively. Conclusions: By appropriate feature Selection and by hyperparameter tunning increase the diabetes prediction accuracy at an early stage.

Centers for Disease Control and Prevention Investments in Adverse Childhood Experience Prevention Efforts