Anthony Costa Constantinou1, Barbaros Yet2, Norman Fenton2, Martin Neil2, William Marsh2. 1. Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK. Electronic address: anthony@constantinou.info. 2. Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
Abstract
OBJECTIVES: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. METHOD: The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. RESULTS: The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). CONCLUSIONS: We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.
OBJECTIVES: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. METHOD: The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. RESULTS: The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). CONCLUSIONS: We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.
Authors: Francesco Bellocchio; Caterina Lonati; Jasmine Ion Titapiccolo; Jennifer Nadal; Heike Meiselbach; Matthias Schmid; Barbara Baerthlein; Ulrich Tschulena; Markus Schneider; Ulla T Schultheiss; Carlo Barbieri; Christoph Moore; Sonja Steppan; Kai-Uwe Eckardt; Stefano Stuard; Luca Neri Journal: Int J Environ Res Public Health Date: 2021-11-30 Impact factor: 3.390