As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Length of Stay Prediction in Neurosurgery with Russian GPT-3 Language Model Compared to Human Expectations
Authors
Gleb Danilov, Konstantin Kotik, Elena Shevchenko, Dmitriy Usachev, Michael Shifrin, Yulia Strunina, Tatyana Tsukanova, Timur Ishankulov, Vasiliy Lukshin, Alexander Potapov
Patients, relatives, doctors, and healthcare providers anticipate the evidence-based length of stay (LOS) prediction in neurosurgery. This study aimed to assess the quality of LOS prediction with the GPT3 language model upon the narrative medical records in neurosurgery comparing to doctors’ and patients’ expectations. We found no significant difference (p = 0.109) between doctors’, patients’, and model’s predictions with neurosurgeons tending to be more accurate in prognosis. The modern neural network language models demonstrate feasibility in LOS prediction.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.