To provide the best treatment, a physician needs information about both the patient and the medicines matching the patient status and improving it. In this article, we present three methods for structuring the sections of medical prospectuses using neural networks. To structure the information from a medical prospectus we use 3 web sources with structured data from sections (with names sections from prospectuses and with uniformized names of sections) to train as input for neural networks. The tests were conducted on Romanian prospectuses. After running the three algorithms, the prospectuses were compared in terms of accuracy and execution time for each source. It was concluded that the accuracy is higher in convolutional networks and in the case of uniform name sections. The output data is used in applications with decision support for the treatment, matching best treatment with the patient’s status.
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