  In ophthalmology, intravitreal operative medication therapy (IVOM) is a
widespread treatment for diseases related to the age-related macular
degeneration (AMD), the diabetic macular edema (DME), as well as the retinal
vein occlusion (RVO). However, in real-world settings, patients often suffer
from loss of vision on time scales of years despite therapy, whereas the
prediction of the visual acuity (VA) and the earliest possible detection of
deterioration under real-life conditions is challenging due to heterogeneous
and incomplete data. In this contribution, we present a workflow for the
development of a research-compatible data corpus fusing different IT systems of
the department of ophthalmology of a German maximum care hospital. The
extensive data corpus allows predictive statements of the expected progression
of a patient and his or her VA in each of the three diseases. For the disease
AMD, we found out a significant deterioration of the visual acuity over time.
Within our proposed multistage system, we subsequently classify the VA
progression into the three groups of therapy "winners", "stabilizers", and
"losers" (WSL classification scheme). Our OCT biomarker classification using an
ensemble of deep neural networks results in a classification accuracy
(F1-score) of over 98 %, enabling us to complete incomplete OCT documentations
while allowing us to exploit them for a more precise VA modeling process. Our
VA prediction requires at least four VA examinations and optionally OCT
biomarkers from the same time period to predict the VA progression within a
forecasted time frame, whereas our prediction is currently restricted to IVOM /
no therapy. We achieve a final prediction accuracy of 69 % in macro average
F1-score, while being in the same range as the ophthalmologists with 57.8 and
50 +- 10.7 % F1-score.
