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Causal knowledge engineering: A case study from COVID-19 (2403.14100v1)

Published 21 Mar 2024 in cs.AI

Abstract: COVID-19 appeared abruptly in early 2020, requiring a rapid response amid a context of great uncertainty. Good quality data and knowledge was initially lacking, and many early models had to be developed with causal assumptions and estimations built in to supplement limited data, often with no reliable approach for identifying, validating and documenting these causal assumptions. Our team embarked on a knowledge engineering process to develop a causal knowledge base consisting of several causal BNs for diverse aspects of COVID-19. The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE). The CKE provides a structured approach for building a causal knowledge base that can support the development of a variety of application-specific models. Here we describe the CKE method, and use our COVID-19 work as a case study to provide a detailed discussion and analysis of the method.

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References (38)
  1. Dynamic Models for Coronavirus Disease 2019 and Data Analysis. Mathematical Methods in the Applied Sciences 2020, 43, 4943–4949, [https://onlinelibrary.wiley.com/doi/pdf/10.1002/mma.6345]. https://doi.org/10.1002/mma.6345.
  2. Early Dynamics of Transmission and Control of COVID-19: A Mathematical Modelling Study. The Lancet Infectious Diseases 2020, 20, 553–558. https://doi.org/10.1016/S1473-3099(20)30144-4.
  3. Ensemble Learning Model for Diagnosing COVID-19 from Routine Blood Tests. Informatics in Medicine Unlocked 2020, 21, 100449. https://doi.org/10.1016/j.imu.2020.100449.
  4. Dynamic Causal Modelling of Immune Heterogeneity. Scientific Reports 2021, 11, 11400. https://doi.org/10.1038/s41598-021-91011-x.
  5. Modeling COVID-19 Disease Processes by Remote Elicitation of Causal Bayesian Networks from Medical Experts 2023. 23, 76. https://doi.org/10.1186/s12874-023-01856-1.
  6. Bridging the Gaps in Test Interpretation of SARS-CoV-2 through Bayesian Network Modelling 2021. 149, e166. https://doi.org/10.1017/S0950268821001357.
  7. Bayesian Artificial Intelligence, 2 ed.; CRC Press, Inc., 2010.
  8. Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference; Morgan Kaufmann, 1988.
  9. Causal Inference: What If; Chapman & Hall/CRC: Boca Raton, 2020.
  10. Knowledge Engineering: Principles and Methods. Data & Knowledge Engineering 1998, 25, 161–197. https://doi.org/10.1016/S0169-023X(97)00056-6.
  11. The Philosophy of Computer Simulation. In Proceedings of the Logic, Methodology and Philosophy of Science: Proceedings of the Thirteenth International Congress. College Publications, 2009, pp. 306–325.
  12. Building Bayesian Networks through Ontologies. In Proceedings of the ECAI, 2002, Vol. 2002, p. 15th.
  13. A Causal Mapping Approach to Constructing Bayesian Networks. Decis. Support Syst. 2004, 38, 259–281.
  14. Fenz, S. An Ontology-Based Approach for Constructing Bayesian Networks. Data & Knowledge Engineering 2012, 73, 73–88. https://doi.org/10.1016/j.datak.2011.12.001.
  15. Novel Coronavirus 2019 (Covid-19) Epidemic Scale Estimation: Topological Network-Based Infection Dynamics Model. medRxiv : the preprint server for health sciences 2020. https://doi.org/10.1101/2020.02.20.20023572.
  16. Bramstedt, K.A. The Carnage of Substandard Research during the COVID-19 Pandemic: A Call for Quality. Journal of Medical Ethics 2020, 46, 803–807. https://doi.org/10.1136/medethics-2020-106494.
  17. CmapTools: A Knowledge Modeling and Sharing Environment 2004.
  18. Research Commentary: Information Systems and Conceptual Modeling—A Research Agenda. Information Systems Research 2002, 13, 363–376. https://doi.org/10.1287/isre.13.4.363.69.
  19. Moody, D.L. Theoretical and Practical Issues in Evaluating the Quality of Conceptual Models: Current State and Future Directions. Data & Knowledge Engineering 2005, 55, 243–276. https://doi.org/10.1016/j.datak.2004.12.005.
  20. Davies, M. Concept Mapping, Mind Mapping and Argument Mapping: What Are the Differences and Do They Matter? Higher Education 2011, 62, 279–301. https://doi.org/10.1007/s10734-010-9387-6.
  21. Srinivas, S. A Probabilistic Approach to Hierarchical Model-Based Diagnosis. In Uncertainty Proceedings 1994; Elsevier, 1994; pp. 538–545.
  22. Srinivas, S. A Generalization of the Noisy-Or Model. In Uncertainty in Artificial Intelligence; Elsevier, 1993; pp. 208–215.
  23. Object-Oriented Bayesian Networks. In Proceedings of the Proc. 13th Conference on Uncertainty in Artificial Intelligence (UAI), 1997, pp. 302–313.
  24. Building Large-Scale Bayesian Networks. The Knowledge Engineering Review 2000, 15, 257–284. https://doi.org/10.1017/S0269888900003039.
  25. Skyrms, B. Causal Decision Theory. The Journal of Philosophy 1982, 79, 695–711, [2026547]. https://doi.org/10.2307/2026547.
  26. Laskey, K.B. MEBN: A Language for First-Order Bayesian Knowledge Bases. Artificial Intelligence 2008, 172, 140–178. https://doi.org/10.1016/j.artint.2007.09.006.
  27. PR-OWL–a Language for Defining Probabilistic Ontologies. International Journal of Approximate Reasoning 2017, 91, 56–79.
  28. Causation, Prediction, and Search; Vol. 81, Lecture Notes in Statistics, Springer New York: New York, NY, 1993. https://doi.org/10.1007/978-1-4612-2748-9.
  29. Good Practice in Bayesian Network Modelling. Environmental Modelling & Software 2012, 37, 134–145. https://doi.org/10.1016/j.envsoft.2012.03.012.
  30. Burgman, M.A. Trusting Judgements: How to Get the Best out of Experts; Cambridge University Press, 2016.
  31. O’Hagan, A. Expert Knowledge Elicitation: Subjective but Scientific. The American Statistician 2019, 73, 69–81. https://doi.org/10.1080/00031305.2018.1518265.
  32. IDEA: Investigate, Discuss, Estimate, Aggregate for Structured Expert Judgement. International Journal of Forecasting 2016.
  33. How Many Experts? IEEE Intelligent Systems 2016, 31, 56–62. https://doi.org/10.1109/MIS.2016.95.
  34. Recruiting Participants for Population Health Intervention Research: Effectiveness and Costs of Recruitment Methods for a Cohort Study. Journal of medical Internet research 2021, 23, e21142. https://doi.org/10.2196/21142.
  35. Learning Linear Causal Models by MML Sampling. In Causal Models and Intelligent Data Management; Springer, 1999; pp. 89–111.
  36. Incorporating Expert Elicited Structural Information in the CaMML Causal Discovery Program. Technical report, 2006.
  37. The Practice of Qualitative Parameterisation in the Development of Bayesian Networks, 2024, [arxiv:cs.AI/2402.12887].
  38. European Food Safety Authority. Guidance on Expert Knowledge Elicitation in Food and Feed Safety Risk Assessment. EFSA Journal 2014, 12. https://doi.org/10.2903/j.efsa.2014.3734.
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Authors (7)
  1. Steven Mascaro (4 papers)
  2. Yue Wu (339 papers)
  3. Ross Pearson (2 papers)
  4. Owen Woodberry (2 papers)
  5. Jessica Ramsay (1 paper)
  6. Tom Snelling (1 paper)
  7. Ann E. Nicholson (7 papers)

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