Застосування технологій автоматичного планування для наповнення бази знань медичного профілю
dc.citation.epage | 198 | |
dc.citation.issue | 12 | |
dc.citation.journalTitle | Вісник Національного університету "Львівська політехніка" "Інформаційні системи та мережі" | |
dc.citation.spage | 177 | |
dc.contributor.affiliation | Національний університет “Львівська політехніка” | |
dc.contributor.affiliation | ФМІ ім. Г. В. Карпенка Національної академії наук України | |
dc.contributor.affiliation | Університет принца Саттама бін Абдулазіза | |
dc.contributor.affiliation | Lviv Polytechnic National University | |
dc.contributor.affiliation | PMI in. G.V. Karpenka of the National Academy of Sciences of Ukraine | |
dc.contributor.affiliation | Prince Sattam Bin Abdulaziz University | |
dc.contributor.author | Досин, Дмитро | |
dc.contributor.author | Яценко, Андрій | |
dc.contributor.author | Ковалевич, Віра | |
dc.contributor.author | Dosyn, Dmytro | |
dc.contributor.author | Yatsenko, Andriy | |
dc.contributor.author | Kovalevych, Vira | |
dc.contributor.author | Daradkeh, Yousef Ibrahim | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2025-03-06T08:06:20Z | |
dc.date.created | 2022-02-28 | |
dc.date.issued | 2022-02-28 | |
dc.description.abstract | Широке впровадження інтелектуальних систем підтримки прийняття рішень гальмується відсутністю методів і технологій автоматичного наповнення бази знань під час експлуатації таких систем. Особливо гостра ця проблема у медичній галузі. Її вирішення – у площині застосування технологій автоматичного планування. Напрацьовані у цій галузі методи й алгоритми обчислення оптимальної стратегії розв’язання задач, які строго сформульовані у термінах логіки предикатів, дають змогу чисельно оцінювати корисність нових повідомлень і, завдяки цьому, ранжувати інформацію за важливістю та автоматично відбирати суттєву, щоб внести її до бази знань. У роботі запропоновано архітектуру медичної ІСППР, яка реалізує цей підхід, обґрунтовано застосовність марковського наближення для формалізації задач автоматичного планування у медичній галузі, ефективність запропонованого підходу показано на прикладі поінформованого вибору сироватки для вакцинування від грипу. | |
dc.description.abstract | The widespread implementation of intelligent decision support systems (IDSS) is hampered by the lack of methods and technologies for automatically filling the knowledge base during the operation of such systems. This problem is especially acute in the medical field. Its solution lies in the application of automatic planning technologies. The methods and algorithms developed in this field for estimation the optimal strategy for solving problems, which are strictly formulated in terms of predicate logic, allow numerically evaluating the usefulness of new messages and thus ranking information by importance and automatically selecting essential information for entering it into the knowledge base. The paper proposes the architecture of a medical IDSS that implements this approach, substantiates the applicability of the Markov approximation for the formalization of automatic planning tasks in the medical field, shows the effectiveness of the proposed approach using the example of an informed choice of serum for influenza vaccination. | |
dc.format.extent | 177-198 | |
dc.format.pages | 22 | |
dc.identifier.citation | Досин Д. Застосування технологій автоматичного планування для наповнення бази знань медичного профілю / Дмитро Досин, Андрій Яценко, Віра Ковалевич // Вісник Національного університету "Львівська політехніка" "Інформаційні системи та мережі". — Львів : Видавництво Львівської політехніки, 2022. — № 12. — С. 177–198. | |
dc.identifier.citationen | Dosin D. Application of automated planning technologies for completing the medical knowledge base / Dosyn Dmytro, Yatsenko Andriy, Kovalevych Vira, Daradkeh Yousef Ibrahim // Visnyk Natsionalnoho universytetu "Lvivska politekhnika" "Informatsiini systemy ta merezhi". — Lviv : Lviv Politechnic Publishing House, 2022. — No 12. — P. 177–198. | |
dc.identifier.doi | doi.org/10.23939/sisn2022.12.177 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/63956 | |
dc.language.iso | uk | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Вісник Національного університету "Львівська політехніка" "Інформаційні системи та мережі", 12, 2022 | |
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dc.relation.references | 39. Della Cioppa G, Vesikari T, Sokal E, Lindert K, Nicolay U. (2011). Trivalent and quadrivalent MF59®-adjuvanted influenza vaccine in young children: A dose- and schedule-finding study. Vaccine, Vol. 29, Is. 47, 8696–8704. https://doi.org/10.1016/j.vaccine.2011.08.111. | |
dc.relation.referencesen | 1. Fellbaum, C. (1998). WordNet: An Electronic Lexical Database Cambridge: Bradford Books. | |
dc.relation.referencesen | 2. Miller, G. A. (1995). Wordnet: A lexical database for English. Communications of the ACM, Vol. 38, No. 11, 39–41. | |
dc.relation.referencesen | 3. Bodenreider, O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004 Jan. 1;32(Database issue): D267–70. DOI: 10.1093/nar/gkh061. PMID: 14681409; PMCID: PMC308795. | |
dc.relation.referencesen | 4. Shankar. R. D., Martins, S. B., O'Connor, M., Parrish, D. B., Das, A. K. An ontology-based architecture for integration of clinical trials management applications. AMIA Annu Symp Proc. 2007 Oct 11;2007:661-5. PMID: 18693919; PMCID: PMC2655871. | |
dc.relation.referencesen | 5. Knop, M., Weber, S., Mueller, M., Niehaves, B. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review JMIR Hum Factors 2022;9(1):e28639. DOI: 10.2196/28639. | |
dc.relation.referencesen | 6. Shepard, D. M. et al. Clinical implementation of an automated planning system for gamma knife radiosurgery. International Journal of Radiation Oncology, Biology, Physics, Vol. 56, Is. 5, 1488–1494, DOI: https://doi.org/10.1016/S0360-3016(03)00440-1. | |
dc.relation.referencesen | 7. Schmidt, M. C. et al. Technical Report: Development and Implementation of an Open Source Template Interpretation Class Library for Automated Treatment Planning. Practical Radiation Oncology, Vol. 12, Is. 2, e153–e160. DOI: https://doi.org/10.1016/j.prro.2021.11.004. | |
dc.relation.referencesen | 8. Spyropoulos, C. D. (2000). AI planning and scheduling in the medical hospital environment. Artificial intelligence in medicine, 20(2), 101–111. https://doi.org/10.1016/s0933-3657(00)00059-2. | |
dc.relation.referencesen | 9. Barbagallo, S., Corradi, L., de Ville de Goyet, J., Iannucci, M., Porro, I., Rosso, N., Tanfani, E., & Testi, A. (2015). Optimization and planning of operating theatre activities: an original definition of pathways and process modeling. BMC medical informatics and decision making, 15, 38. https://doi.org/10.1186/s12911-015-0161-7. | |
dc.relation.referencesen | 10. Teixeira M. S., Maran, V., Dragoni, M. (2020). The interplay of a conversational ontology and AI planning for health dialogue management. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery, New York, NY, USA, 611–619. DOI: https://doi.org/10.1145/3412841.3441942. | |
dc.relation.referencesen | 11. Torres Silva, E. A., Uribe, S., Smith, J., Luna Gomez, I. F., Florez-Arango, J. F. XML Data and Knowledge Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study – JMIR Form Res 2020;4(10):e17512doi: 10.2196/17512 PMID: 33064087 PMCID: 7600017. | |
dc.relation.referencesen | 12. Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, Vol. 46, Is. 4, 744–763. ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2013.06.009. | |
dc.relation.referencesen | 13. Samwald, M., Fehre, K., de Bruin, J., Adlassnig, K.-P. (2012). The Arden Syntax standard for clinical decision support: Experiences and directions. Journal of Biomedical Informatics, Vol. 45, Is. 4, 711–718. ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2012.02.001. | |
dc.relation.referencesen | 14. Foster, M. E., Petrick, R. P. A. Towards Using Social HRI for Improving Children's Healthcare Experiences. In: Proceedings of the AAAI Fall Symposium on Artificial Intelligence for Human-Robot Interaction (AI HRI 2020), Arlington, Virginia, USA, November 2020. | |
dc.relation.referencesen | 15. McDermott, D., Ghallab, M., Howe, A., Knoblock, C.A., Ram, A., Veloso, M., Weld, D., and Wilkins, D. PDDL – The Planning Domain Definition Language, Technical Report CVC TR-98-003 / DCS TR-1165, Yale Center for Communicational Vision and Control, October 1998. | |
dc.relation.referencesen | 16. Papazoglou, M., Pohl, K., Parkin, M., and Metzger, A. (Eds.). 2010. Service research challenges and solutions for the future internet: S-cube – towards engineering, managing and adapting service-based systems. Springer Verlag, Berlin, Heidelberg. | |
dc.relation.referencesen | 17. Srinivasan, N., Paolucci, M., Sycara, K. (2006). Semantic Web Service Discovery in the OWL-S IDE, in: Proceedings of the 39th Hawaii InternationalConference on System Sciences. | |
dc.relation.referencesen | 18. Graham, S. (2004). Building web services with Java: making sense of XML, SOAP, WSDL, and UDDI. [Indianapolis, Ind.]. Sams. http://www.myilibrary.com?id=86268. | |
dc.relation.referencesen | 19. Alarcos, A. O., Beßler, D., Khamis, A. M., Gonçalves, P., Habib, M. K., Bermejo-Alonso, J., Barreto, M. E., Diab, M., Rosell, J., Quintas, J., Olszewska, J. I., Nakawala, H., Freitas, E. P., Gyrard, A., Borgo, S., Alenyà, G., Beetz, M., & Li, H. (2019). A review and comparison of ontology-based approaches to robot autonomy. Knowledge Eng. Review, 34, e29. | |
dc.relation.referencesen | 20. van Leeuwen D, Mittelman M, Fabian L, Lomotan EA. Nothing for Me or About Me, Without Me: Codesign of Clinical Decision Support. Appl Clin Inform. 2022 May;13(3):641–646. DOI: 10.1055/s-0042-1750355. Epub 2022 Jun 29. PMID: 35768012; PMCID: PMC9242738. | |
dc.relation.referencesen | 21. Malik, G., Dana, N., Traverso, P. (2004). Automated Planning Theory & Practice. San Francisco: Morgan Knaufman, 635 p. | |
dc.relation.referencesen | 22. Russell, S. J., Norvig, P. (2009). Artificial Intelligence: a modern approach. Pearson. | |
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dc.relation.referencesen | 38. Höpping, A. M., Fonville, J. M., Russell, C. A., James, S., Smith, D. J. (2016). Influenza B vaccine lineage selection. An optimized trivalent vaccine. Vaccine, Vol. 34, Is. 13, 1617–1622, https://doi.org/10.1016/j.vaccine.2016.01.042. | |
dc.relation.referencesen | 39. Della Cioppa G., Vesikari T., Sokal E., Lindert K., Nicolay U. (2011). Trivalent and quadrivalent MF59®-adjuvanted influenza vaccine in young children: A dose- and schedule-finding study. Vaccine, Vol. 29, Is. 47, 8696–8704. https://doi.org/10.1016/j.vaccine.2011.08.111. | |
dc.relation.uri | https://doi.org/10.1016/S0360-3016(03)00440-1 | |
dc.relation.uri | https://doi.org/10.1016/j.prro.2021.11.004 | |
dc.relation.uri | https://doi.org/10.1016/s0933-3657(00)00059-2 | |
dc.relation.uri | https://doi.org/10.1186/s12911-015-0161-7 | |
dc.relation.uri | https://doi.org/10.1145/3412841.3441942 | |
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dc.relation.uri | https://doi.org/10.1016/j.jbi.2012.02.001 | |
dc.relation.uri | http://www.myilibrary.com?id=86268 | |
dc.relation.uri | http://aimlab.cs.uoregon.edu/services/owl2pddl/martini_honors_ | |
dc.relation.uri | https://doi.org/10.1016/j.vaccine.2016.01.042 | |
dc.relation.uri | https://doi.org/10.1016/j.vaccine.2011.08.111 | |
dc.relation.uri | http://aimlab.cs.uoregon.edu/services/owl2pddl/martini_honors_thesis_SPRING_2013.pdf | |
dc.rights.holder | © Національний університет “Львівська політехніка”, 2022 | |
dc.rights.holder | © Досин Д., Яценко А., Ковалевич В., Daradkeh Y. I., 2022 | |
dc.subject | база знань | |
dc.subject | автоматичне планування | |
dc.subject | навчання онтології | |
dc.subject | інтелектуальна система підтримки прийняття рішень | |
dc.subject | knowledge base | |
dc.subject | automated planning | |
dc.subject | ontology learning | |
dc.subject | intelligent decision support system | |
dc.subject | POMDP | |
dc.subject.udc | 004.67 | |
dc.title | Застосування технологій автоматичного планування для наповнення бази знань медичного профілю | |
dc.title.alternative | Application of automated planning technologies for completing the medical knowledge base | |
dc.type | Article |
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