PREDICTING MEDICINE DEMAND USING DEEP LEARNING TECHNIQUES

Medication supply and storage are essential components of the medical industry and distribution. Most medications have a predetermined expiration date. When the demand is met in large quantities that exceed the actual need, this leads to the accumulation of medicines in the stores, and this leads to the expiration of the materials. If demand is too low, this will have an impact on consumer happiness and drug marketing.Therefore, it is necessary to find a way to predict the actual quantity required for the organization's needs to avoid material spoilage and storage problems. A mathematical prediction model is required to assist any management in achieving the required availability of medicines for customers and safe storage of medicines. The research question is to design a system based on deep learning that can predict the amount of drugs required with high efficiency and accuracy based on the chronology of previous years.Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) are used to build prediction models. Those models allow for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures such as mean squared error (MSE), mean absolute squared error (MASE), root mean squared error (RMSE), and others are used to evaluate the prediction models. RNN model achieved the best result with MSE: 0.019 MAE: 0.102, RMSE: 0.0

The impact of machine learning on the efficiency of the B2B sales service in pharmaceutical companies

The explanatory study examines the possible value of Machine Learning in the B2B sales process in pharmaceutical companies. Sales representatives accounting for a wide range of activities, suffering from time consuming and repetitive tasks. This study investigates the potential of Machine Learning applications for B2B sales in order to facilitate sales representative’s daily tasks and enhance the entire sales process. The results have been obtained through qualitative research based on 8 interviews with AI-experts, pharma consultants and sales representatives as well as secondary data in form of academic articles and reports. The findings reveal that, compared to other departments, ML-applications in B2B sales are less applied at the current stage, but mostly in the customer service process. The interviews have shown that the usage of ML-applications is possible within all steps of the sales process and enhances its overall efficiency and effectivity in terms of time, costs and quality. Furthermore, tasks which increase the efficiency of the sales department through ML applications are outlined. By applying ML within the B2B sales process, the daily work of sales representatives can be facilitated, which ultimately could not only have a positive impact on customer satisfaction, but also on employee commitment leading to competitive advantage in the price intense environment of the pharmaceutical industry.O presente estudo foca-se na possível importância da Aprendizagem Automática no serviço de vendas B2B em Empresas Farmacêuticas. Representantes de vendas responsáveis por uma grande variedade de actividades, afectados pelas demoradas e longas tarefas. Esta dissertação examina o potêncial da Aprendizagem Automática nas vendas B2B a fim de facilitar as tarefas diárias dos representantes de vendas, e de melhorar ainda todo o processo de vendas. Os resultados são obtidos através de uma pesquisa qualitativa baseada em 10 entrevistas com AI-experts, consultantes farmacêuticos e representantes de vendas, assim como fichas de dados provenientes de artigos e relatórios. Os resultados revelam que, em comparação com outros departamentos, a aplicação da Aprendizagem Automática em vendas B2B são actualmente menos aplicadas, sobretudo no que diz respeito ao atendimento ao cliente. As entrevistas mostraram que o uso da Aprendizagem Automática é possível em todas as fases do processo de vendas sendo que melhora toda a sua eficiência e efetividade em termos de tempo, custos e qualidade. Posteriormente, as tarefas de vendas mais eficientes dentro das farmácias estão estabelecidas; pelo que, a introdução da Aprendizagem Automática dentro do processo de vendas B2B poderá facilitar e, inclusive, melhorar o trabalho dos representantes de vendas, sendo que esta otimazação poderá, por conseguinte, não só ter um impacto positivo na satisfação do cliente como também no compromisso dos empregados originando, desta forma, uma vantagem competitiva face ao intenso ambiente de preços na industria farmacêutica

Central bank misperceptions and the role of money in interest rate rules

Research with Keynesian-style models has emphasized the importance of the output gap for policies aimed at controlling inflation while declaring monetary aggregates largely irrelevant. Critics, however, have argued that these models need to be modified to account for observed money growth and inflation trends, and that monetary trends may serve as a useful cross-check for monetary policy. We identify an important source of monetary trends in form of persistent central bank misperceptions regarding potential output. Simulations with historical output gap estimates indicate that such misperceptions may induce persistent errors in monetary policy and sustained trends in money growth and inflation. If interest rate prescriptions derived from Keynesian-style models are augmented with a cross-check against money-based estimates of trend inflation, inflation control is improved substantially.monetary policy under uncertainty, money, output gap uncertainty, quantity theory and Taylor rules

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD.This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm.The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers.Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm