Modelling of Causal Relations in Human Pathophysiology for Medical Education and Design Inspiration

  • Soumya Singh
  • Aditi Makharia
  • Amaresh Chakrabarti
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 135)


While knowledge of general biological phenomena attracted much attention for supporting bio-inspired design, knowledge of biomedical problems has hardly been explored. However, this too can support training, diagnosis, and design. For instance, clinical expertise development during pre-clinical training depends on biomedical knowledge organized as complex causal structures in the clinician’s mind. The accuracy of diagnostic expertise depends on the richness of these causal structures. Knowledge of medical problems can also act as stimuli for ideation for a designer. However, there is a lack of a standard structure for the depiction of causal relations. The proposed model for causal relations in human pathophysiology aims at describing the causes and consequences of diseases to aid pre-clinical medical education and design inspiration. The model is based on a ‘systems thinking’ approach to studying a complex system, where the entities of a biological system, arranged hierarchically in multiple levels, interact to fulfil a function. The model incorporates three levels of description of a disease: Hierarchical component-issue level, issue inter-relation level and issue description level. The first level incorporates knowledge of the hierarchy of all related components and issues occurring in a disease. The second level captures relational knowledge among the issues. The third level provides data at the most detailed level of human biology (biochemical processes) for each issue in a disease. Each level is accompanied with textual descriptions and images to aid comprehension. A qualitative survey is conducted with medical students and faculty members to assess the usability of the model.


  1. 1.
    Schmidt, H.G., Rikers, R.M.J.P.: How expertise develops in medicine: knowledge encapsulation and illness script formation. Med. Educ. 41(12), 1133–1139 (2007). URL
  2. 2.
    Schmidt, H.G., Boshuizen, H.PA.: On acquiring expertise in medicine. Educ. Psychol. Rev. 5(3), 205–221 (1993). URL Scholar
  3. 3.
    Lesgold, A., Rubinson, H., Feltovich, P., Glaser, R., Klopfer, D., Wang, Y.: Expertise in a complex skill: diagnosing x-ray pictures. The Nat. Expert. 311–342 (1988)Google Scholar
  4. 4.
    Hmelo, C.E., Holton, D.L., Kolodner, J.L.: Designing to learn about complex systems. The J. Learn. Sci. 9(3), 247–298 (2000). URL Scholar
  5. 5.
    Hmelo-Silver, C.E., Azevedo, R.: Understanding complex systems: some core challenges. The J. Learn. Sci. 15(1), 53–61 (2006)CrossRefGoogle Scholar
  6. 6.
    Von Bertalanffy, L.: The history and status of general systems theory. Acad. Manag. J. 15(4), 407–426 (1972). URL
  7. 7.
    Boersma, K.Th., Waarlo, A.J.: Systems thinking as a metacognitive tool for students, teachers and curriculum developers, in Proceedings of V ESERA Conference, pp. 1–16. Noordwijkerhout, Netherlands, 19–23 Aug 2003. URL
  8. 8.
    Miller, P.L., Fisher, P.R.: Causal models in medical artificial intelligence. Sel. Top. Med. Artif. Intell. 11–24 (1988). URL
  9. 9.
    Chakrabarti, A., Sarkar, P., Leelavathamma, B., Nataraju, B.S.: A functional representation for aiding biomimetic and artificial inspiration of new ideas. AI EDAM 19(2) (2005). URL
  10. 10.
    Chakrabarti, A., Siddharth, L., Dinakar, M., Palegar, N., Ranganath, R.: A tool for capturing and structuring rationale, in 32nd Symposium-STC Jointly Conducted by Indian Institute of Science (IISc) and Indian Space Research Organization (ISRO), p. 4. Bangalore, India, 2006Google Scholar
  11. 11.
    Chakrabarti, A., Siddharth, L., Makharia, A., Singh, S., Ranganath, R., CRISP—Capturing Rationale as Issues and Solutions using Systemic: Sequential and SAPPhIRE view-based rePresentation, in 33rd Symposium-STC Jointly Conducted by Indian Institute of Science (IISc) and Indian Space Research Organization (ISRO), p. 6. Bangalore, India, 2017Google Scholar
  12. 12.
    Saladin, K.S.: Human Anatomy. McGraw-Hill, Boston (2008)Google Scholar
  13. 13.
    VanPutte, C., Regan, J., Russo, A.: Seeley’s Essentials of Anatomy & Physiology. McGraw-Hill, New York (2016)Google Scholar
  14. 14.
    Clement, C.A., Yanowitz, K.L.: Using an analogy to model causal mechanisms in a complex text. Instr. Sci. 31(3), 195–225 (2003). URL
  15. 15.
    Chung, S.: Basal cell carcinoma. Arch. Plastic Surg. 39(2), 166–170 (2012). Scholar
  16. 16.
    Sehgal, V.N., Chatterjee, K.: Basal cell carcinoma: pathophysiology. SKINmed 12(3), 176–181 (2014)Google Scholar
  17. 17.
    Kitano, H.: Computational systems biology. Nature 420(6912), 206–210 (2002)CrossRefGoogle Scholar
  18. 18.
    Bolander, L.K., Lonka, K., Josephson, A.: How do medical teachers address the problem of transfer? Adv. Health Sci. Educ. 13(3), 345–360 (2008). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Soumya Singh
    • 1
  • Aditi Makharia
    • 1
  • Amaresh Chakrabarti
    • 1
  1. 1.Innovation, Design Study and Sustainability Laboratory (IdeasLab)Centre for Product Design and Manufacturing, Indian Institute of ScienceBengaluruIndia

Personalised recommendations