dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Siebes, Arno | |
dc.contributor.author | Hoek, Marit | |
dc.date.accessioned | 2024-08-07T23:06:07Z | |
dc.date.available | 2024-08-07T23:06:07Z | |
dc.date.issued | 2024 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/47142 | |
dc.description.abstract | A pre-trained Transformer was inplemented wirh the aim of optimizing the extraction of diagnosis classifications from Dutch psychiatric texts. Within this process, the following attributes were compared: data used for pre-training, the classification method and oversampling. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Using LLMs to extract diagnoses from clinical texts in psychiatry | |
dc.title | Using LLMs to extract diagnoses from clinical texts in psychiatry | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Transformer; Large Language Model; Psychiatry; Diagnosis Classification | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 36204 | |