  Millions of unsolicited medical inquiries are received by pharmaceutical
companies every year. It has been hypothesized that these inquiries represent a
treasure trove of information, potentially giving insight into matters
regarding medicinal products and the associated medical treatments. However,
due to the large volume and specialized nature of the inquiries, it is
difficult to perform timely, recurrent, and comprehensive analyses. Here, we
propose a machine learning approach based on natural language processing and
unsupervised learning to automatically discover key topics in real-world
medical inquiries from customers. This approach does not require ontologies nor
annotations. The discovered topics are meaningful and medically relevant, as
judged by medical information specialists, thus demonstrating that unsolicited
medical inquiries are a source of valuable customer insights. Our work paves
the way for the machine-learning-driven analysis of medical inquiries in the
pharmaceutical industry, which ultimately aims at improving patient care.
