Preparing clinical ophthalmic data for research application
| dc.contributor.author | Rößner, Miriam | |
| dc.contributor.author | Kahl, Stefan | |
| dc.contributor.author | Engelmann, Katrin | |
| dc.contributor.author | Kowerko, Danny | |
| dc.contributor.editor | Eibl, Maximilian | |
| dc.contributor.editor | Gaedke, Martin | |
| dc.date.accessioned | 2017-08-28T23:47:46Z | |
| dc.date.available | 2017-08-28T23:47:46Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | This paper presents an analysis of clinical examination, diagnostic and patient data belonging to persons with eye diseases like age-related macular degeneration (AMD). Our purpose is to investigate potential correlations of extracted features to discover their impacts on the disease. This is a first step to the predictability of the progression of AMD based on a heterogeneous data set. We focus on the visual acuity as reasonable indicator for the progression of this disease and analyse its temporal trend to classify patients in winners, stabilisers and losers.We describe the retrieval of textual medical reporting data for optical coherence tomography images and evaluate the machine-readable categorisation of these texts. Additionally, we address the topic of ethical guidelines for the work with patients’ data and discuss the potential and limitations of our data set in the context of obtaining structured (mass) data for training neural networks as future perspective. | en |
| dc.identifier.doi | 10.18420/in2017_222 | |
| dc.identifier.isbn | 978-3-88579-669-5 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2017 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-275 | |
| dc.subject | Age-related macular degeneration | |
| dc.subject | Ophthalmology | |
| dc.subject | Text mining | |
| dc.subject | Data visualisation | |
| dc.subject | AMD progression prediction | |
| dc.title | Preparing clinical ophthalmic data for research application | en |
| gi.citation.endPage | 2240 | |
| gi.citation.startPage | 2231 | |
| gi.conference.date | 25.-29. September 2017 | |
| gi.conference.location | Chemnitz | |
| gi.conference.sessiontitle | Deep Learning in heterogenen Datenbeständen |
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