Please use this identifier to cite or link to this item:
https://repository.iimb.ac.in/handle/2074/19979
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Venkatagiri, Shankar | |
dc.contributor.author | Mitra, Neel Ratan | |
dc.date.accessioned | 2021-06-21T14:51:36Z | - |
dc.date.available | 2021-06-21T14:51:36Z | - |
dc.date.issued | 2019 | |
dc.identifier.uri | https://repository.iimb.ac.in/handle/2074/19979 | - |
dc.description.abstract | Medical prescriptions are a rich source of data for Pharmaceutical companies & other stakeholders in the healthcare industry to gain insights on how their products & services are performing. For instance, as per the category of drug molecules being prescribed by doctors pharma companies can gauge the stage (growing, declining, etc.) of the life-cycle their drugs are at. Certain drugs molecules may be gaining favours across different specialty (e.g.: Cardiologist, Diabetologists, etc.) - being in possession of such knowledge would allow firms to realign their sales strategy to drive more drug prescription; and in turn generate more sales. | |
dc.publisher | Indian Institute of Management Bangalore | |
dc.relation.ispartofseries | PGP_CCS_P19_107 | |
dc.subject | Artificial intelligence | |
dc.subject | Pharmaceutical industry | |
dc.subject | Machine learning | |
dc.subject | Healthcare industry | |
dc.title | Applying machine learning/Artificial intelligence in pharmaceutical data research | |
dc.type | CCS Project Report-PGP | |
dc.pages | 15p. | |
Appears in Collections: | 2019 |
Files in This Item:
File | Size | Format | |
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PGP_CCS_P19_107.pdf | 909.44 kB | Adobe PDF | View/Open Request a copy |
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