Abstract
Natural language processing is being pressed into use to facilitate the selection
of cases for medical research in electronic health record databases, though study inclusion criteria may be complex, and the linguistic cues indicating eligibility may be subtle. Finding cases of first episode psychosis raised a number of problems for automated approaches, providing an opportunity to explore how machine learning technologies might be used to overcome them. A system was delivered that achieved an AUC of 0.85, enabling 95% of relevant cases to be identified whilst halving the work required in manually reviewing cases. The techniques that made this possible are presented.
of cases for medical research in electronic health record databases, though study inclusion criteria may be complex, and the linguistic cues indicating eligibility may be subtle. Finding cases of first episode psychosis raised a number of problems for automated approaches, providing an opportunity to explore how machine learning technologies might be used to overcome them. A system was delivered that achieved an AUC of 0.85, enabling 95% of relevant cases to be identified whilst halving the work required in manually reviewing cases. The techniques that made this possible are presented.
| Original language | English |
|---|---|
| Pages | 196-205 |
| Publication status | Published - 2016 |
| Event | 15th Workshop on Biomedical Natural Language Processing - Berlin, Germany Duration: 12 Aug 2016 → … http://www.aclweb.org |
Workshop
| Workshop | 15th Workshop on Biomedical Natural Language Processing |
|---|---|
| Country/Territory | Germany |
| City | Berlin |
| Period | 12/08/16 → … |
| Internet address |