About this Research Topic
Medication plays an important role in medical treatment. While medication brings benefits to patients, it also brings risks or harms. Traditional clinical trials for medication consume huge financial costs and manpower costs. A large amount of medication treatment information is generated in the daily clinical work. The utilization of the information has very high-cost effectiveness for medication research. However, there is little information about medication in various medical databases. This is mainly due to the variety of medications, the complexity of medication treatment, and the difficulty in presenting the data structure of medication use. These make data mining in pharmaceutical research difficult. The unavailability of clinical information about medication is a great waste of resources. Correspondingly, compared with data mining in medicine, there are fewer types of research and achievements in pharmaceutical data mining. Therefore, the purpose of this research topic is to explore how to use big data for efficient pharmaceutical research, to provide help for the establishment of a better database, and to develop data mining technology for pharmaceutical research.
We welcome the original research, reviews, and mini reviews, including but not limited to:
• The establishment method of a database for drug prevention, treatment, and adverse reactions.
• Optimization of pharmaceutical data structure in a database.
• Data mining practice for pharmaceutical research.
Note: Drugs in the Research Topic refer to ones with a single active ingredient. We do not accept studies involving drugs with multiple active ingredients or unknown active ingredients.
Keywords: Data mining, Pharmaceutical research, Clinical big data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.