| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Ruigrok, Ynte | |
| dc.contributor.author | Edwards, Laurens | |
| dc.date.accessioned | 2022-11-17T00:00:35Z | |
| dc.date.available | 2022-11-17T00:00:35Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/43205 | |
| dc.description.abstract | Predicting the outcome of aneurysmal subarachnoid hemorrhage could play an important role in the
management of aneurysms. As developing prediction models becomes easier an uptake in model
development is taking place in science. This also increases the amount of methodological errors made
during development leading to difficulty in reproduction or clinical usage of these models. In this
review papers that developed a model predicting the outcome of aneurysmal subarachnoid hemorrhage
were selected | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | Predicting the outcome of aneurysmal subarachnoid hemorrhage could play an important role in the
management of aneurysms. As developing prediction models becomes easier an uptake in model
development is taking place in science. This also increases the amount of methodological errors made
during development leading to difficulty in reproduction or clinical usage of these models. In this
review papers that developed a model predicting the outcome of aneurysmal subarachnoid hemorrhage
were selected | |
| dc.title | Using the PROBAST tool to identify potential methodological biases in studies
developing prediction models for the outcome of aneurysmal subarachnoid
hemorrhage. | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | Machine learning; subarachnoid; hemorrhage; haemorrhage; probast; prediction models; model; | |
| dc.subject.courseuu | Bioinformatics and Biocomplexity | |
| dc.thesis.id | 12038 | |
