Application of automated planning technologies for completing the medical knowledge base

: pp. 177 - 198
Lviv Polytechnic National University
Department of Computer Engineering and Networks, College of Engineering at Wadi Addawasir 11991, Prince Sattam Bin Abdulaziz University, KSA

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.

  1. Fellbaum, C. (1998). WordNet: An Electronic Lexical Database Cambridge: Bradford Books.
  2. Miller, G. A. (1995). Wordnet: A lexical database for English. Communications of the ACM, Vol. 38, No. 11, 39–41.
  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.
  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.
  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.
  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:
  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:
  8. Spyropoulos, C. D. (2000). AI planning and scheduling in the medical hospital environment. Artificial intelligence in medicine, 20(2), 101–111.
  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.
  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:
  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.
  12. Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, Vol. 46, Is. 4, 744–763. ISSN 1532-0464,
  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,
  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.
  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.
  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.
  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.
  18. Graham, S. (2004). Building web services with Java: making sense of XML, SOAP, WSDL, and UDDI. [Indianapolis, Ind.]. Sams.
  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.
  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.
  21. Malik, G., Dana, N., Traverso, P. (2004). Automated Planning Theory & Practice. San Francisco: Morgan Knaufman, 635 p.
  22. Russell, S. J., Norvig, P. (2009). Artificial Intelligence: a modern approach. Pearson.
  23. Braziunas, D. (2003). POMDP solution methods: technical report. Toronto: University of Toronto, 24 р.
  24. Li, H., Liao, X., Carin, L. (2006). Incremental Least Squares Policy Iteration for POMDPs. AAAI. – AAAI Press. Palm Springs, 1167–1172.
  25. Poupart, P., Boutilier, C. (2003). Value-directed compression of POMDPs. NIPS, 15.
  26. Spaan, M., Vlassis, N. (2005). Perseus: Randomized point-based value iteration for POMDPs. JAIR, 24, 195–220.
  27. Martini, A. (2013). Integrating Metadata and Data Syntax Translation. Computer and Information Science Department. University of Oregon. Режим доступу:
  28. 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.
  29. Fiscus, J. G., Doddington, G., Garofolo, J. S., and Martin, A.. Nist’s 1998 topic detection and tracking evaluation (tdt2). In Proc. of the DARPA Broadcast News Workshop, Virginia, US, 1998.
  30. Stratonovych, R. L. (1965). About the value of information. Izvestiya AN USSR, Technical Cybernetics, No. 5, 3–12.
  31. Kharkevich, A. A. (1960). About the value of information. Problems of Cybernetics, Is. 4, 53–57.
  32. Kopkin, E. V., Kobzarev, I. M. (2019). Using Stratonovich's information value measure for optimization of flexible programs for diagnosing technical objects.Tr. SPIIRAN, 18:6, 1434–1461.
  33. Korohodin, V. I., Korohodin, V. L. (2000). Information as the basis of life. Dubna: Fenix. 208 p.
  34. Stratonovych, R. L. (1975). Information theory. M.: Sov. radio, 424 p.
  35. Dosyn, D. H. (2018). The architecture of the pertinence assessment system based on learning the ontology of planning in the selected subject area. Information Extraction and Processing, No. 46 (122), 61–67.
  36. Dosyn, D. G. (2018). The relevance of information as the value of knowledge for an intellectual agent. Journal of Lviv Polytechnic National University: Information Systems and Networks, No. 901, 111–117.
  37. Hubbard, D. (2007). How to Measure Anything: Finding the Value of Intangibles in Business.  John Wiley & Sons,
  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,
  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.