Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department
Anna Hillenmayer, Bjoern Lofi, Sabrina Langhans, Carolin Elhardt, Armin Wolf, Christian Maximilian Wertheimer · 2026
Read Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department by Anna Hillenmayer, Bjoern Lofi, Sabrina Langhans, Carolin Elhardt, Armin Wolf, Christian Maximilian Wertheimer (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12772578.txt.
Description
Anna Hillenmayer and co-authors teach a neural network to grade urgency in an eye emergency room in this 2025 study. Sorting who needs care first guards safety and saves resources. The team used language processing to read patients' own words.
On arrival, 1,715 patients filled a form with free text and checkboxes. Staff labeled each case urgent or not from the final diagnosis. A BERT network trained on most forms and was tested on the rest. The study checks how well the model matches expert sorting. A reliable tool could speed triage, though human judgment stays in charge of the final call.
Catalog record
| CREATOR | Anna Hillenmayer, Bjoern Lofi, Sabrina Langhans, Carolin Elhardt, Armin Wolf, Christian Maximilian Wertheimer |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 31 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |