Enviar artigo por via de e-mail The possibility to use artificial intelligence methods in predicting the outcomes of neurosurgical operations
- Autores: Zabezhailo M.I.1, Gavrjushin A.V.2
-
Afiliações:
- Federal research center “Computer science and control” of Russian Academy of Sciences
- N.N. Burdenko Neurosurgery Centre of the Ministry of Health of the Russian Federation
- Edição: Nº 2 (2024)
- Páginas: 37-52
- Seção: AI-enabled Systems
- URL: https://bakhtiniada.ru/2071-8594/article/view/265368
- DOI: https://doi.org/10.14357/20718594240203
- EDN: https://elibrary.ru/JXIDPS
- ID: 265368
Citar
Texto integral
Resumo
Some abilities of artificial intelligence methods application in predicting the outcomes of neurosurgical operations are discussed. The presented approach is based on the analysis of causal similarity as a basis for generation cause-and-effect dependencies initially hidden in accumulated empirical data. The mathematical formalization of this heuristic is constructed by clarifying similarity as a binary algebraic operation used to compare descriptions of precedents and search in them for approximate representation of the causality of target effects – the outcomes of neurosurgical operations. The possibilities of the presented approach are illustrated by the results of an intelligent analysis of real empirical data covering a series of neurosurgical operations of stem tumors performed in 2005-2018 at the N.N. Burdenko National Medical Research Center for Neurosurgery.
Sobre autores
Michael Zabezhailo
Federal research center “Computer science and control” of Russian Academy of Sciences
Autor responsável pela correspondência
Email: m.zabezhailo@yandex.ru
Doctor of physical and mathematical sciences, Head of Department
Rússia, MoscowAndrey Gavrjushin
N.N. Burdenko Neurosurgery Centre of the Ministry of Health of the Russian Federation
Email: avg.avg03@gmail.com
Candidate of medical sciences. Neurosurgeon, Researcher of the glial tumours department
Rússia, MoscowBibliografia
- Pearl J. Causality: models, reasoning, and inference. N.-Y.: Cambridge University Press, 2000. P. 384.
- Höfler M. Causal inference based on counterfactuals. BMC Med Res Methodol, 2005. V. 5. No 28. https://bmcme- dresmethodol.biomedcentral.com/articles/10.1186/1471- 2288-5-28.
- Evidence-Based Medicine Working Group. Evidence-based medicine. A new approach to teaching the practice of medicine // JAMA. 1992. V. 268. No 17. P. 2420–2425.
- Howick J.H. The Philosophy of Evidence-based Medicine. Chichester (UK): J.Wiley & Sons, 2011. P. 244.
- Talantov P. 0,05 Dokazatelnaya medicina ot magyi do poiskov bessmaetiya [0,05 evidence-based medicine from magic to the quest for immortality]. М: Corpus, 2019. P. 629.
- Pearson K. On Lines and Planes of Closest Fit to Systems of Points in Space // Philosophical Magazine. 1901. No 2 (11). P. 559–572.
- Shapley L.S. A value for n-person games. Santa Monica (CA): RAND Corporation. 1952. P. 15.
- Finn V.K. J.S. Mill’s inductive methods in artificial intelli gence systems // Scientific and Technical Information Pro cessing. Part I. 2011. V. 38. No 6. P. 385–402. Part II. 2012. V. 39. No 5. P. 241–260.
- Finn V.K. Intellekt, informatsionnoe obshchestvo, gumani tarnoe znanie i obrazovanie [Intelligence, Inform. Society, and Humanities Knowledge and Education]. Moscow: LENAND, 2021.
- Cohn P.M. Universal algebra. Springer (NL), 1981. P. 414.
- Pigeonhole principle. Herstein I. N. Topics In Algebra. Waltham: Blaisdell Publishing Company, 1964. P. 342.
- Peirce C.S. The Essential Peirce, Selected Philosophical Writings (Nathan Houser and Christian J. W. Kloesel, eds.). Indiana University Press, Bloomington and Indianapolis, IN. 1992.
- Zabezhailo M.I., Trunin Yu.Yu. On the Reliability of Medical Diagnosis Based on Empirical Data // Scientific and Technical Information Processing. 2021. V. 48. No 5. P. 1–8.
- Zabezhailo M.I. 2022. Ustoychivost empiricheskikh zavisi mostey i problema ob’yasneniya resultatov intellektualnogo analyza dannikh [Stability of empirical dependences and problem of explaining the results of intelligent analysis of data] // Integrirovannye modeli i myagkie vychisleniya v is-kusstvennom intellekte. Sbornik nauchnykh trudov XI Mezhdunarodnoi nauchno-prakticheskoi konferentsii [Inte grated Models and Soft Computing in Artificial Intelligence: Proceedings of 11th Int. Sci.-Pract. Conf.]. Moscow: RAII, 2022. No 2. P. 50–59.
- Zabezhailo M.I. On the Problem of AI-Tools Application in Digital Control Systems// Automatic Documentation and Mathematical Linguistics. 2022. V. 56. No 5. P. 229–236.
- Tarski A. The Semantic Conception of Truth and the Foun dations of Semantics // Philosophy and Phenomenological Research. 1944. V. 4. No 3. P. 341–375. https://sites.ual berta.ca/~francisp/Phil426/TarskiTruth1944.pdf
Arquivos suplementares
