Abstract
Clinical pathways play a crucial role in guiding the treatment of specific medical conditions or patient populations, but often rely on basic textual documentation, leading to potential inefficiencies and delays in patient care. This paper reports the early stages of a research aiming at exploring the application of knowledge representation techniques in the digitalization of diagnostic and therapeutic care pathways. These techniques are used to annotate contextual data, patient information and medical guidelines with respect to a reference ontology. In this way, a comprehensive knowledge graph can be processed using rule-based approaches to support the patient care management process, providing physicians and medical practitioners with valuable insights about specific diseases.
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References
Alahmar, A.D., Benlamri, R.: SNOMED CT-based standardized e-clinical pathways for enabling big data analytics in healthcare. IEEE Access 8, 92765–92775 (2020)
Ardito, C., Bellifemine, F., Di Noia, T., Lofu, D., Mallardi, G.: A proposal of case-based approach to clinical pathway modeling support. In: 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1–6. IEEE (2020)
Bediang, G., Falquet, G., Geissbuhler, A.: An ontology-based semantic model for sharing and reusability of clinical pathways across context (ShaRE-CP). In: MEDINFO 2021: One World, One Health–Global Partnership for Digital Innovation, pp. 86–90. IOS Press (2022)
Bender, D., Sartipi, K.: HL7 FHIR: an agile and RESTful approach to healthcare information exchange. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 326–331 (2013)
Blandford, A.: HCI for health and wellbeing: challenges and opportunities. Int. J. Hum Comput Stud. 131, 41–51 (2019)
Braunstein, M.L.: Health care in the age of interoperability: the potential and challenges. IEEE Pulse 9(5), 34–36 (2018)
Corcho, O., Fernandez-Lopez, M., Gomez-Perez, A.: Ontological engineering: what are ontologies and how can we build them? In: Semantic Web Services: Theory, Tools and Applications, pp. 44–70. IGI Global (2007)
Donnelly, K., et al.: SNOMED-CT: the advanced terminology and coding system for eHealth. Stud.n Health Technol. Inform. 121, 279 (2006)
Eberhart, A., Slogeris, B., Sadreameli, S., Jassal, M.: Using a human-centered design approach for collaborative decision-making in pediatric asthma care. Public Health 170, 129–132 (2019)
El-Sappagh, S., Franda, F., Ali, F., Kwak, K.S.: SNOMED CT standard ontology based on the ontology for general medical science. BMC Med. Inform. Decis. Mak. 18, 1–19 (2018)
Etemadi, M., et al.: A systematic review of healthcare recommender systems: open issues, challenges, and techniques. Expert Syst. Appl. 213, 118823 (2023)
Folino, F., Pizzuti, C.: A recommendation engine for disease prediction. IseB 13(4), 609–628 (2014)
Garabedian, P.M., Gannon, M.P., Aaron, S., Wu, E., Burns, Z., Samal, L.: Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. BMC Med. Inform. Decis. Mak. 22(1), 1–12 (2022)
Graves, M., Constabaris, A., Brickley, D.: FOAF: connecting people on the semantic web. Cat. Classif. Q. 43(3–4), 191–202 (2007)
Guha, R.V., Brickley, D., Macbeth, S.: Schema. org: evolution of structured data on the web. Commun. ACM 59(2), 44–51 (2016)
Haddad, S.M., et al.: Building a digital tool for the adoption of the World Health Organization’s antenatal care recommendations: methodological intersection of evidence, clinical logic, and digital technology. J. Med. Internet Res. 22(10), e16355 (2020)
Hoehn, M.M., Yahr, M.D.: Parkinsonism: onset, progression and mortality. Neurology 17(5), 427–427 (1967)
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: a semantic web rule language combining OWL and RuleML. W3C Member Submission, W3C, May 2004. https://www.w3.org/Submission/SWRL/
International Organization for Standardization: Ergonomics of human-system interaction – Part 210: Human-centred design for interactive systems. ISO 9241-210:2019 edn. (2019). https://www.iso.org/standard/77520.html
Lampropoulos, I.C., Malli, F., Sinani, O., Gourgoulianis, K.I., Xiromerisiou, G.: Worldwide trends in mortality related to Parkinson’s disease in the period of 1994–2019: analysis of vital registration data from the WHO mortality database. Front. Neurol. 13 (2022)
Matheson, G.O., Pacione, C., Shultz, R.K., Klügl, M.: Leveraging human-centered design in chronic disease prevention. Am. J. Prev. Med. 48(4), 472–479 (2015)
McDonald, C.J., et al.: LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin. Chem. 49(4), 624–633 (2003)
Melles, M., Albayrak, A., Goossens, R.: Innovating health care: key characteristics of human-centered design. Int. J. Qual. Health Care 33(Supplement_1), 37–44 (2021)
Nazar, M., Alam, M.M., Yafi, E., Su’ud, M.M.: A systematic review of human-computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access 9, 153316–153348 (2021)
Patton, E., Van Woensel, W., Seneviratne, O., Loseto, G., Scioscia, F., Kagal, L.: Development of AI-enabled apps by patients and domain experts using the punya platform: a case study for diabetes. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds.) Artificial Intelligence in Medicine. AIME 2022. LNCS, vol. 13263, pp. 431–435. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09342-5_45
Quinn, J.: An HL7 (Health Level Seven) overview. J. AHIMA 70(7), 32-4; quiz 35-6 (1999)
Rudolph, S., Hitzler, P., Patel-Schneider, P., Krötzsch, M., Parsia, B.: OWL 2 Web Ontology Language Primer (Second Edition). W3C Recommendation, W3C, December 2012. https://www.w3.org/TR/owl2-primer/
Schreiber, G., Raimond, Y.: RDF 1.1 Primer. W3C Working Group Note, W3C, June 2014. https://www.w3.org/TR/rdf11-primer/
Schriml, L.M., et al.: The human disease ontology 2022 update. Nucleic Acids Res. 50(D1), 255–261 (2021)
Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical owl-dl reasoner. J. Web Semant. 5(2), 51–53 (2007)
Sun, H., et al.: Semantic processing of EHR data for clinical research. J. Biomed. Inform. 58, 247–259 (2015)
Tran, T.N.T., Felfernig, A., Trattner, C., Holzinger, A.: Recommender systems in the healthcare domain: state-of-the-art and research issues. J. Intell. Inf. Syst. 57(1), 171–201 (2020)
Vagal, A., et al.: Human-centered design thinking in radiology. J. Am. Coll. Radiol. 17(5), 662–667 (2020)
Van Woensel, W., Arndt, D., Tomaszuk, D., Bialystok, L., Kellogg, G.: Notation3. Draft Community Group Report, W3C, April 2023. https://w3c.github.io/N3/spec/
Van Woensel, W., Scioscia, F., Loseto, G., Seneviratne, O., Patton, E., Abidi, S.: Explanations of symbolic reasoning to effect patient persuasion and education. In: Third International Workshop on eXplainable Artificial Intelligence in Healthcare (XAI-Health), co-located with 21st International Conference of Artificial Intelligence in Medicine – AIME 2023 (2023)
Woensel, W.V., et al.: Explainable clinical decision support: towards patient-facing explanations for education and long-term behavior change. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds.) Artificial Intelligence in Medicine. AIME 2022. LNCS, vol. 13263, pp. 57–62. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09342-5_6
Verborgh, R., De Roo, J.: Drawing conclusions from linked data on the web: the EYE reasoner. IEEE Softw. 32(3), 23–27 (2015)
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This work has been partially supported by project T.I.C.P. (Technology for Integrated Care Pathways) and program RIPARTI, both funded by the Apulia Region.
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Loseto, G. et al. (2024). Towards a Knowledge-Based Approach for Digitalizing Integrated Care Pathways. In: Bramwell-Dicks, A., Evans, A., Winckler, M., Petrie, H., Abdelnour-Nocera, J. (eds) Design for Equality and Justice. INTERACT 2023. Lecture Notes in Computer Science, vol 14535. Springer, Cham. https://doi.org/10.1007/978-3-031-61688-4_8
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