Bilska, Magdalena (2022) Extracting drug indications from Structured Product Labels using Deep Learning techniques. Master's Thesis / Essay, Computational Cognitive Science.
|
Text
Thesis_UMC (1).pdf Download (13MB) | Preview |
|
![[img]](https://fse.studenttheses.ub.rug.nl/style/images/fileicons/text.png) |
Text
toestemming.pdf Restricted to Registered users only Download (124kB) |
Abstract
Pharmacovigilance (PV) is defined as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems. PV utilizes various methods and technologies, such as statistical approaches of disproportionality calculation, as well as drug classification systems, normalized medical terminologies, and modern techniques of handling and extracting unstructured data like Deep Learning (DL). In this thesis, several DL models have been developed and tested to accomplish the task of mining drug indications (reasons to take a dug) from official Food and Drug Agency (FDA) labels. The motivation was to improve the data mining processes of Uppsala Monitoring Centre (UMC), the World Health Organization Collaborating Centre for International Drug Monitoring.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Supervisor name: | Spenader, J.K. and Jones, S.M. |
| Degree programme: | Computational Cognitive Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 20 Oct 2022 10:27 |
| Last Modified: | 20 Oct 2022 10:27 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/28865 |
Actions (login required)
 |
View Item |