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Predicting the Length of Stay in Neurosurgery with RuGPT-3 Language Model
Gleb Danilov1, Konstantin Kotik1, Elena Shevchenko1
1Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
Abstract:
In this study, we update the evaluation of the Russian GPT3 model presented in our previous paper in predicting the length of stay (LOS) in neurosurgery. We aimed to assess the performance the Russian GPT-3 (ruGPT-3) language model in LOS prediction using narrative medical records in neurosurgery compared to doctors' and patients' expectations. Doctors appeared to have the most realistic LOS expectations (MAE = 2.54), while the model's predictions (MAE = 3.53) were closest to the patients' (MAE = 3.47) but inferior to them (p = 0.011). A detailed analysis showed a solid quality of ruGPT-3 performance based on narrative clinical texts. Considering our previous findings obtained with recurrent neural networks and FastText vector representation, we estimate the new result as important but probably improveable.
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