Abstract
Decision support systems (DSS) support clinical reasoning with evidence-based recommendations. In pediatric epileptology, integrating multimodal data-EEG, seizure semiology, therapy, imaging, etc.-can improve diagnostic consistency and guide therapy selection beyond EEG-only approaches. Advances in attention-based deep learning enable the detection of complex patterns in real-world data while preserving clinician oversight and alignment with ILAE standards. Our goal is to develop and validate an AI-enabled DSS that supports standardized diagnosis and treatment decision-making in children and adolescents with epilepsy, and to establish an open, scalable framework for multi-center external validation.
| Original language | English |
|---|---|
| Journal | Neuropediatrics - Journal of Pediatric Neurobiology, Neurology and Neurogenetics |
| DOIs | |
| Publication status | E-pub ahead of print - 27 Apr 2026 |
Fields of science
- 302035 Paediatrics and adolescent medicine
- 102022 Software development
- 502050 Business informatics
- 102040 Quantum computing
- 509026 Digitalisation research
- 211928 Systems engineering
- 102034 Cyber-physical systems
- 502032 Quality management
- 102020 Medical informatics
- 102006 Computer supported cooperative work (CSCW)
- 102027 Web engineering
- 102016 IT security
- 503015 Subject didactics of technical sciences
- 102015 Information systems
- 301405 Neuropathology
- 302052 Neurology
- 302051 Neurosurgery
- 301114 Cell biology
JKU Focus areas
- Digital Transformation
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