Journal "Software Engineering"
a journal on theoretical and applied science and technology
ISSN 2220-3397

Issue N6 2024 year

DOI: 10.17587/prin.15.308-321
Knowledge Graph Formation Using LLM for Disease Treatment in Decision Support Systems
V. V. Gribova, D. Sc. (Technical Sciences), Deputy Director, Head of intelligent systems lab, gribova@iacp.dvo.ru, Institute of Automation and Control Processes, Far Eastern Branch of Russian, Academy of Sciences, Vladivostok, 690041, Russian Federation, Far Eastern Federal University, Vladivostok, 690922, Russian Federation, V. S. Perevolotsky, Postgraduate Student, lost.yayo@gmail.com, Far Eastern Federal University, Vladivostok, 690922, Russian Federation
Corresponding author: Vladimir S. Perevolotsky, Postgraduate Student, Far Eastern Federal University, Vladivostok, 690922, Russian Federation, E-mail: lost.yayo@gmail.com
Received on February 13, 2024
Accepted on March 26, 2024

The article presents an advanced method for creating knowledge graphs, applicable in medical decision support systems. It focuses on adapting the method to various clinical guidelines, making it particularly valuable in the medi­cal field. The developed universal ontological pattern allows for the formation of knowledge bases for a wide range of diseases, making the process flexible and scalable without the need for specific programming. This approach significantly reduces the labor intensity of creating medical support systems and facilitates their integration and adaptation to various clinical conditions and requirements. The practical significance of the method was confirmed in the management systems for the therapy of various diseases, including allergic rhinitis, underscoring its universality and applicability in the current medical context. The effectiveness of the proposed approach opens new perspectives for the development and improvement of medical information systems, contributing to the improvement of patient care and treatment quality. Thus, the article makes a significant contribution to the advancement of information technology in medicine, offering a solution that can be adapted for a wide range of medical applications and research.

Keywords: artificial intelligence, clinical decision support systems, knowledge graphs, ontology, personalized treatment, treatment management automation, machine learning in medicine, LLM
pp. 308—321
For citation:
Gribova V. V., Perevolotsky V. S. Knowledge Graph Formation Using LLM for Disease Treatment in Decision Support Systems, Programmnaya Ingeneria, 2024, vol. 15, no. 6, pp. 308—321. DOI: 10.17587/prin.15.308-321. (in Russian).
The work was carried out within the framework of the state assignment of the Institute of Automated Control of the Far Eastern Branch of the Russian Academy of Sciences (topic FWFW-2021-0004 — an approach for translating models of regulatory documents into a machine-understandable representation using LLM).
References:
  1. Chen Z., Liang N., Zhang H. et al. Harnessing the power of clinical decision support systems: challenges and opportunities, Open Heart, 2023, vol. 10, no. 2, article e002432. DOI: 10.1136/openhrt-2023-002432.
  2. Shahsavarani A. M., Azad Marz Abadi E., Hakimi Kalk-horan M. et al. Clinical decision support systems (CDSSs): state of the art review of literature, International Journal of Medical Reviews, 2015., vol. 2, no. 4, pp. 299—308.
  3. Moja L., Kwag K. H., Lytras T. et al. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis, American journal of public health, 2014, vol. 104, no. 12, pp. e12—е22. DOI: 10.2105/ AJPH.2014.302164.
  4. Potseluev E. L., Gorbunov A. E. Medical error and crime: equivalent concepts? Nauka. Obshhestvo. Gosudarstvo, 2017, vol. 5, no. 1, pp. 128—135 (in Russian).
  5. Makhambetchin M. M., Shakeev K. T. Some aspects of the theory of physician errors, Zdravoohranenie Rossijskoj Federacii, 2021, vol. 65, no. 2, pp. 159—165. DOI: 10.47470/0044-197X-2021-65-2-159-165 (in Russian).
  6. Rostovtsev V. N., Kobrinskii B. A. Principles and Possible Ways of Building an Intelligent System of Integral Medicine, Open Semantic Technologies for Intelligent Systems, 2021, no. 5. pp. 225—228.
  7. Jia Qu. A Review on the Application of Knowledge Graph Technology in the Medical Field, Scientific Programming, 2022, vol. 2022, article ID 3212370, p. 12. DOI: 10.1155/2022/3212370.
  8. Zhi Qi. A review on the construction and application of medical knowledge graph, Applied and Computational Engi­neering, 2023,vol. 4, no. 1, pp. 618—622. DOI: 10.54254/2755­2721/4/2023361.
  9. Rajabi E., Kafaie S. Knowledge Graphs and Explainable AI in Healthcare, Information, 2022, vol. 13, no. 10, article 459. DOI: 10.3390/info13100459.
  10. Chen X., Liu J., Xu L. et al. Knowledge graph of CO-VID-19 patient activity-a case study of Zhengzhou city, Journal of Wuhan Univ, 2020, vol. 45, pp. 816—824.
  11. Jiajing Wu. Construct a Knowledge Graph for China Coronavirus (COVID-19) Patient Information Tracking, Risk Management and Healthcare Policy, 2021, vol. 2021:14, pp. 4321—4337. DOI: 10.2147/RMHP.S30973.
  12. Zhao H., Chen J., Huang L. et al. Automatic Generation of Medical Report with Knowledge Graph, In 2021 10th International Conference on Computing and Pattern Recognition (ICCPR '21), 2021, October 15—17, Shanghai, China, 2021, pp. 1—10.
  13. Chen T., Zhang Y., Qian X. et al. A knowledge graph-based method for epidemic contact tracing in public transportation, Transportation Research Part C: Emerging Technologies, 2022, vol. 137, article 103587. DOI: 10.1016/j.trc.2022.103587.
  14. Zhang P., Bu Y., Jiang P. et al. Toward a Coronavirus Knowledge Graph, Genes, 2021, vol. 12, no. 7, article 998. DOI: 10.3390/genes12070998.
  15. Yang Y., Cao Z., Zhao P. et al. Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study, Journal of Safety Science and Resilience, 2022, vol. 137, pp. 146—156. DOI: 10.1016/j.jnlssr.2021.08.002.
  16. Gribova V. V., Moskalenko P. M., Timchenko V. A., Shalfeeva E. A. The IACPaaS Platform for Developing Systems Based on Ontologies: A Decade of Use, Scientific and Technical Information Processing, 2023, vol. 50, no. 5, pp. 1—7. DOI: 10.3103/ S0147688223050064.
  17. Taejin Kim, Yeoil Yun, Namgyu Kim. Deep Learning-Based Knowledge Graph Generation for COVID-19, Sustainability, 2021, vol. 13, no. 4, article 2276. DOI: 10.3390/su13042276.
  18. Tao X., Pham T., Zhang J. et al. Mining health knowledge graph for health risk prediction, World Wide Web, 2020, vol. 23, pp. 1—22. DOI: https://doi.org/10.1007/s11280-020-00810-1.
  19. Chi Y., Yu C., Qi X. et al. Knowledge Management in Healthcare sustainability: a Smart Healthy Diet Assistant in Tra­ditional Chinese Medicine Culture, Sustainability, 2018, vol. 10, no. 11, article 4197. DOI: 10.3390/su10114197.
  20. Rossanez A., dos Reis J. C., Torres R.d.S. et al. KGen: a knowledge graph generator from biomedical scientific literature, BMC Med Inform Decis. 2020, vol. 20 (suppl 4), article 314. DOI: 10.1186/s12911-020-01341-5.
  21. Wu X., J. Duan Y., Pan Y. et al. Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications, Big Data Mining and Analytics. 2023. vol. 6, no. 2, рр. 201—217. DOI: 10.26599/BDMA.2022.9020021.
  22. Tong R., Sun C., Wang H. Construction of traditional Chinese, medical, knowledge, graph and its application, Journal of Medical Information, 2016, vol. 37, no. 4, рр. 8—13. DOI: 10.1155/2022/3212370.
  23. Abu-Salih B., AL-Qurishi M., Alweshah M. et al. Health­care knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities, Journal of big data, 2023, vol. 10, no. 1, article 81. DOI: 10.1186/s40537-023-00774-9.
  24. Cui H., Lu J., Wang S. et al. A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises. 2023. DOI: 10.48550/arXiv.2306.04802.
  25. Wang Y., Ye F., Li B., Jin G., Xu D., Li F. UrbanFloodKG: An Urban Flood Knowledge Graph System for Risk Assessment, CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management, 2023, pp. 2574—2584. DOI: 10.1145/3583780.3615105.
  26. Zhong L., Wu J., Li Q. et al. A Comprehensive Survey on Automatic Knowledge Graph Construction, ACM Computing Surveys, 2023, vol. 56, no. 4, article 94, pp. 1—62. DOI: 10.1145/3618295.
  27. Zhong L., Wu J., Li Q., Peng H., Wu X. A Comprehensive Survey on Automatic Knowledge Graph Construction, ACM Computing Surveys, 2022, vol. 36, no. 4, article 66, pp. 1—50. DOI: 10.48550/arXiv.2302.05019.
  28. Melnyk I., Dognin P., Das P. Knowledge Graph Generation From Text. [Preprint] arXiv:2211.1051. November 2022. DOI: 10.48550/arXiv.2211.10511.
  29. Ni P., Okhrati R., Guan S. et al. Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot, Information Systems Frontiers, 2022, vol. 26, pp. 137—156. DOI: 10.1007/s10796-022-10295-0.
  30. Gribova V., Kovalev R., Okun D. A Specialized Shell for Intelligent Systems of Prescribing Medication, Scientific and Technical Information Processing, 2021, vol. 48, no. 5, pp. 1—11. DOI: 10.3103/S0147688221050038.
  31. Gribova V. V., Shalfeeva E. A., Petryaeva M. V. et al. Cloud service for differential diagnosis and personalized treatment of inflammatory heart diseases, Programmnye sistemy: teorija i prilozhenija, 2023, vol. 14, no. 4, pp. 141—188 (in Russian).
  32. Gribova V. V., Kul'chin Y. N., Petryaeva M. V. et al. An Intel­ligent System for Medical Decision Support in Differential Diagnosis and Treatment of COVID-19, Herald of the Russian Academy of Sciences, 2022, vol. 92, no. 4, pp. 511—519. DOI: 10.1134/S1019331622040128.
  33. Borodulina E. A., Gribova V. V., Eremenko E. P. et al. Intelligent service for managing the treatment process of patients with pulmonary tuberculosis, Vrach i informacionnye tehnologii, 2021, no. 2, pp. 36—45. DOI: 10.25881/18110193_2021_2_36 (in Russian).
  34. International statistical classification of diseases and related health problems (10th revision) (ICD-10) (version 2.23 dated 07/19/2023). WHO Moscow Center for International Classification of Diseases, Russian Academy of Medical Sciences Research Insti­tute of Social Hygiene, Economics and Health Management named after. ON THE. Semashko (in Russian).
  35. Jiang X., Zhang R., Xu Y. et al. Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models. 2023. arXiv preprint arXiv:2312.15883.
  36. Gribova V. V., Okun D. B. Ontologies for the formation of knowledge bases and the implementation of therapeutic measures in medical intelligent systems, Informatika i sistemy upravleniya, 2018, no. 3 (57), pp. 71—80. DOI: 10.22250/isu.2018.57.71-80 (in Russian).
  37. Perevolotsky V. S., Kravchuk D. A. Using automated systems to create ontological knowledge bases, Naukoemkie tehnologii v kosmicheskih issledovanijah Zemli, 2024, vol. 16, no. 1, pp. 54—58 (in Russian).