| Autor: |
DANILOV, Gleb, KOTIK, Konstantin, SHIFRIN, Michael, STRUNINA, Uliya, PRONKINA, Tatyana, POTAPOV, Alexander |
| Zdroj: |
Studies in Health Technology & Informatics; 2019, Vol. 258, p125-129, 5p, 1 Chart, 2 Graphs |
| Abstrakt: |
Electronic Health Records (EHRs) conceal a hidden knowledge that could be mined with data science tools. This is relevant for N.N. Burdenko Neurosurgery Center taking the advantage of a large EHRs archive collected for a period between 2000 and 2017. This study was aimed at testing the informativeness of neurosurgical operative reports for predicting the duration of postoperative stay in a hospital using deep learning techniques. The recurrent neuronal networks (GRU) were applied to the word-embedded texts in our experiments. The mean absolute error of prediction in 90% of cases was 2.8 days. These results demonstrate the potential utility of narrative medical texts as a substrate for decision support technologies in neurosurgery. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
