Introduction: The appreciable growth in pharmaceutical expenditure has resulted in multiple initiatives across Europe to lower generic prices and enhance their utilisation. However, considerable variation in their use and prices. Objective: Assess the influence of multiple supply and demand-side initiatives across Europe for established medicines to enhance prescribing efficiency before a decision to prescribe a particular medicine. Subsequently utilise the findings to suggest potential future initiatives that countries could consider. Method: Analysis of different methodologies involving cross national and single country retrospective observational studies on reimbursed use and expenditure of PPIs, statins and renin-angiotensin inhibitor drugs among European countries. Results: Nature and intensity of the various initiatives appreciably influenced prescribing behaviour and expenditure, e.g. multiple measures resulted in reimbursed expenditure for PPIs in Scotland in 2010 56% below 2001 levels despite a 3 fold increase in utilisation and in the Netherlands, PPI expenditure fell by 58% in 2010 vs. 2000 despite a 3-fold increase in utilisation. A similarly picture was seen with prescribing restrictions, i.e. (i) more aggressive follow-up of prescribing restrictions for patented statins and ARBs resulted in a greater reduction in the utilisation of patented statins in Austria vs. Norway and lower utilisation of patented ARBs vs. generic ACEIs in Croatia than Austria. However, limited impact of restrictions on esomeprazole in Norway with the first prescription or recommendation in hospital where restrictions do not apply. Similar findings when generic losartan became available in Western Europe. Conclusions: Multiple demand-side measures are needed to influence prescribing patterns. When combined with supply-side measures, activities can realise appreciable savings. Health authorities cannot rely on a ‘spill over’ effect between classes to affect changes in prescribing

Multiple policies to enhance prescribing efficiency for established medicines in Europe with a particular focus on demand-side measures: findings and future implications

Alzheimer is a progressive disease and it is the most prevalent neurodegenerative disorder. It is believed that the people with mild cognitive impairment are at high risk of developing this disease. According to the annual report released by the Alzheimer’s Association®2020, Alzheimer is the sixth leading cause of death in the United States. Thus, there is a need of educating people about this disease, reducing the risks by militating the necessary precautions to disseminate its affect by diagnosing it at early stages. It is also important to propose some recent advancement in this research which can help in early prediction of the disease using machine learning techniques. This paper intends to develop the novel algorithm by proposing changes in the designing of capsule network for best prediction results and making the model computationally efficient. The research is conducted on the Open Access Series of Imaging Studies (OASIS) dataset with dimensions (373 X 15) to diagnose the labels into two groups, as demented and non-demented. The novelty lies in conducting the in-depth research in identifying the importance of features, correlation study between factors and density of data showing status of factors by studying hierarchical examination of all the data points available using exploratory data analysis. Several optimization functions are conducted on the variables and feature selection is done to make the model faster and more accurate. The claims have been validated by showing the correlation accuracy at several iterations and layers with an admissible accuracy of 92.39%. The model is compared with state-of-art deep learning classifiers taken as benchmarks using different performance metrics. The ablation study is conducted on the proposed model using OASIS dataset to justify the predictions of the model.

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Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset

The technological transformation has directly affected the functional areas of companies. This circumstance has been a challenge for corporate accounting, since the emerging technology allows handling a large volume of data, and providing valuable information for operational management, managerial control, and strategic planning. The aim of this study is to analyze current and future lines of research globally, during the period 1961–2019, on emerging technologies in corporate accounting. For this, bibliometric techniques were applied to 1126 articles on this subject to obtain findings on scientific production and the main subject areas. Scientific production has increased annually, so that in the last decade de 60.66% of all articles have been published. The main subject areas in which more articles were linked were business, management and accounting, social sciences, and economics, econometrics, and finance. Six lines of research have been identified that generate contributions on this topic. Furthermore, the analysis of the relevance of the keywords has detected the main future directions of research. The increasing worldwide trend of scientific production shows interest in developing aspects of this field of study. This study contributes to the academic, scientific, and professional discussion to improve decision-making based on the available information.

https://www.mdpi.com/2227-7390/8/9/1589/pdf

Research Analysis on Emerging Technologies in Corporate Accounting

Anomalies Detecting in Medical Metrics Using Machine Learning Tools

A Novel Approach for the Automatic Detection of COVID in a Patient by Using a Categorization Methods

The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.

https://www.mdpi.com/2227-7390/8/8/1211/pdf

Data-Driven Analytics for Personalized Medical Decision Making

The study was conducted on the applying of machine learning methods to search for key clusters, which made it possible to investigate individual patient characteristics, and to determine distances between cluster instances. It was suggested to determine the average distance between instances in order to find optimal performance metrics. That will determine the optimal state of the instance of this cluster.

The Applying Processing Intelligence Methods for Classify Persons in Identify Personalized Medication Decisions

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Mariana Zakharchuk

Papers

Data-Driven Analytics for Personalized Medical Decision Making

The Applying Processing Intelligence Methods for Classify Persons in Identify Personalized Medication Decisions

Data Driven Analytics for Personalized Medical Decision Making