[Artificial intelligence guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records]. / Prognozirovanie dlitel'nosti gospitalizatsii v neirokhirurgicheskoi klinike po tekstovym dannym elektronnoi meditsinskoi dokumentatsii s pomoshch'yu tekhnologii iskusstvennogo intellekta.

BACKGROUND: Rational use of internal resources of hospitals including bed fund turnover is important objective in high-tech medicine. Machine learning technologies can improve neurosurgical care and contribute to patient-oriented approach. OBJECTIVE: To evaluate the quality of AI-guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records in comparison with expectations of patients and physicians. MATERIAL AND METHODS: AI-guided prediction was based on analysis of unstructured text records of the electronic medical history (preoperative examination and surgical protocol). Predictive models were learned on the data of the Burdenko Neurosurgery Center accumulated for the period 2000-2017 (90.688 cases). Model testing was performed on 111 completed neurosurgical cases in a prospective study. We compared the accuracy of prediction models compared to expectations of patients and physicians regarding hospital-stay. RESULTS: The median absolute error of machine prediction in the final test was 2.00 days. This value was comparable with the doctor's prediction error. CONCLUSION: This study demonstrated the possibility of using unstructured textual data to predict the length of hospital-stay in a neurosurgical hospital.

[Artificial intelligence technologies in clinical neurooncology]. / Tekhnologii iskusstvennogo intellekta v klinicheskoi neiroonkologii.

Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.

Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology.

Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection. Materials and Methods: CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study. Results: The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370. Conclusion: This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.

Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas.

In recent years, the number of scientific publications on artificial intelligence (AI), primarily on machine learning, with respect to neurosurgery, has increased. The aim of the study was to conduct a systematic literature review and identify the main areas of AI applications in neurosurgery. METHODS: Using the PubMed search engine, we found and analyzed 327 original articles published in 1996-2019. The key words specific to each topic were identified using topic modeling algorithms LDA and ARTM, which are part of the AI-based natural language processing. RESULTS: Five main areas of neurosurgery, in which research into AI methods are underway, have been identified: neuro-oncology, functional neurosurgery, vascular neurosurgery, spinal neurosurgery, and surgery of traumatic brain injury. Specifics of these studies are characterized. CONCLUSION: The information presented in this review can be instrumental in planning new research projects in neurosurgery.

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.

The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery. METHODS: Using the PubMed search engine, 327 original journal articles published from 1996 to July 2019 and related to the use of AI technologies in neurosurgery, were selected. The typical issues addressed by using AI were identified for each area of neurosurgery. RESULTS: The typical AI applications within each of the five main areas of neurosurgery (neuro-oncology, functional, vascular, spinal neurosurgery, and traumatic brain injury) were defined. CONCLUSION: The article highlights the main areas and trends in the up-to-date AI research in neurosurgery, which might be helpful in planning new scientific projects.