Design of an intelligent data analysis platform for pharmaceutical forecasts

This study considers the task to design a data analysis platform with predictive capabilities of neural networks. The object of research is intelligent decision-making systems built on deep learning methods. The proposed intelligent platform takes into account the specificity of working with data in the dynamic and uncertain environment of the pharmaceutical market. Its main purpose is the processing of various data formats, such as time series, regression, classification data sets to create forecasts based on various indicators. At the core of the platform architecture, along with technologies for backend and frontend development (HTML, JS, CSS, C#, .NET), MSSQL Server and TSQL for database management, are AI Microservices (Python, Flask); they are responsible for artificial intelligence services, in particular neural networks. In order to identify the optimal model, which is able to effectively solve regression problems based on the selected indicators, the study analyzed several configurations of neural networks on End-to-end machine learning platforms. Distinctive features of the architecture of the designed data analysis platform include its ability to dynamically switch between different machine learning models based on predefined indicators and criteria such as prediction accuracy and model selection. Improved interpretation of forecasts through the use of Explainable AI enables effective decision-making in the pharmaceutical industry. The functioning of the proposed instrumental decision-making base is demonstrated on the examples of forecasting trends in the consumption of pharmaceuticals by different groups in the pharmaceutical markets of different countries. Automating the model selection and prediction loss minimization process in a comprehensive data analysis platform (CDAP) improves forecast accuracy by approximately 15 % compared to traditional manually selected models.