A Comparison of Word Embeddings to Study Complications in Neurosurgery.

Our study aimed to compare the capability of different word embeddings to capture the semantic similarity of clinical concepts related to complications in neurosurgery at the level of medical experts. Eighty-four sets of word embeddings (based on Word2vec, GloVe, FastText, PMI, and BERT algorithms) were benchmarked in a clustering task. FastText model showed the best close to the medical expertise capability to group medical terms by their meaning (adjusted Rand index = 0.682). Word embedding models can accurately reflect clinical concepts' semantic and linguistic similarities, promising their robust usage in medical domain-specific NLP tasks.

Predicting Postoperative Hospital Stay in Neurosurgery with Recurrent Neural Networks Based on Operative Reports.

This study aimed to predict the duration of the postoperative in-hospital period in neurosurgery based on unstructured operative reports, natural language processing, and deep learning. The recurrent neuronal network (RNN-GRU) was tuned on the word-embedded reports of primary surgical cases retrieved for the period between 2000 and 2017. A new test dataset obtained for the primary operations performed in 2018-2019 was used to evaluate model performance. The mean absolute error of prediction in the final test was 3.00 days. Our study demonstrated the usability of textual EHRs data for the prediction of postoperative period length in neurosurgery using deep learning.

An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language.

Rich-in-morphology language, such as Russian, present a challenge for extraction of professional medical information. In this paper, we report on our solution to identify adverse events (complications) in neurosurgery based on natural language processing and professional medical judgment. The algorithm we proposed is easily implemented and feasible in a broad spectrum of clinical studies.

Prediction of Postoperative Hospital Stay with Deep Learning Based on 101 654 Operative Reports in Neurosurgery.

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.