BackgroundCommunity pharmacies are major contributors to health care systems across the world. Several studies have been conducted to evaluate community pharmacies services in health care. The purpose of this study was to estimate the social and economic benefits of current and potential future community pharmacies services provided by pharmacists in health care in Portugal.MethodsThe social and economic value of community pharmacies services was estimated through a decision-model. Model inputs included effectiveness data, quality of life (QoL) and health resource consumption, obtained though literature review and adapted to Portuguese reality by an expert panel. The estimated economic value was the result of non-remunerated pharmaceutical services plus health resource consumption potentially avoided. Social and economic value of community pharmacies services derives from the comparison of two scenarios: “with service” versus “without service”.ResultsIt is estimated that current community pharmacies services in Portugal provide a gain in QoL of 8.3% and an economic value of 879.6 million euros (M€), including 342.1 M€ in non-remunerated pharmaceutical services and 448.1 M€ in avoided expense with health resource consumption. Potential future community pharmacies services may provide an additional increase of 6.9% in QoL and be associated with an economic value of 144.8 M€: 120.3 M€ in non-remunerated services and 24.5 M€ in potential savings with health resource consumption.ConclusionsCommunity pharmacies services provide considerable benefit in QoL and economic value. An increase range of services including a greater integration in primary and secondary care, among other transversal services, may add further social and economic value to the society.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2525-4) contains supplementary material, which is available to authorized users.

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

Cardiovascular diseases (CVDs) are disorders of the heart and blood vessels and are a major cause of disability and premature death worldwide. Individuals at higher risk of developing CVD must be noticed at an early stage to prevent premature deaths. Advances in the field of computational intelligence, together with the vast amount of data produced daily in clinical settings, have made it possible to create recognition systems capable of identifying hidden patterns and useful information. This paper focuses on the application of Data Mining Techniques (DMTs) to clinical data collected during the medical examination in an attempt to predict whether or not an individual has a CVD. To this end, the CRossIndustry Standard Process for Data Mining (CRISP-DM) methodology was followed, in which five classifiers were applied, namely DT, Optimized DT, RI, RF, and DL. The models were mainly developed using the RapidMiner software with the assist of the WEKA tool and were analyzed based on accuracy, precision, sensitivity, and specificity. The results obtained were considered promising on the basis of the research for effective means of diagnosing CVD, with the best model being Optimized DT, which achieved the highest values for all the evaluation metrics, 73.54%, 75.82%, 68.89%, 78.16% and 0.788 for accuracy, precision, sensitivity, specificity, and AUC, respectively.

Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy.

Hypertension is a major and highly prevalent risk factor for various diseases. Among the most frequently prescribed antihypertensive first‐line drugs are synthetic angiotensin I‐converting enzyme inhibitors (ACEI). However, since  their use in hypertension therapy has been linked to various side effects, interest in the application of food‐derived ACEI peptides (ACEIp) as antihypertensive agents is rapidly growing. Although promising, the industrial production of ACEIp through conventional methods such as chemical synthesis or enzymatic hydrolysis of food proteins has been proven troublesome. We here provide an overview of current antihypertensive therapeutics, focusing on ACEI, and illustrate how biotechnology and bioengineering can overcome the limitations of ACEIp large‐scale production. Latest advances in ACEIp research and current genetic engineering‐based strategies for heterologous production of ACEIp (and precursors) are also presented. Cloning approaches include tandem repeats of single ACEIp, ACEIp fusion to proteins/polypeptides, joining multivariate ACEIp into bioactive polypeptides, and producing ACEIp‐containing modified plant storage proteins. Although bacteria have been privileged ACEIp heterologous hosts, particularly when testing for new genetic engineering strategies, plants and microalgae‐based platforms are now emerging. Besides being generally safer, cost‐effective and scalable, these “pharming” platforms can perform therelevant posttranslational modifications and produce (and eventually deliver) biologically active protein/peptide‐based antihypertensive medicines.