Medicine

Medicine deals with a complex and imperfectly understood system: the human body. While we know a lot about how the body works in health and disease, there remain many info-gaps in our knowledge. Furthermore, the information that a physician has about each individual patient is incomplete. Info-gap theory has been employed to support medical decisions of various sorts.

  • Yakov Ben-Haim, 2006, Info-gap Decision Theory: Decisions Under Severe Uncertainty, 2nd edition, Academic Press, London.
    Chapter 3: Robustness and Opportuneness.
    … Section 3.2.12: Drug selection.
     
  • Chen, W.-L., Kan, C.-D., Yu, F.-M., Mai, Y.-C., Lin, C.-H., 2018, Life-threatening complication detection during hemodialysis using fractional order info-gap decision-making, Intelligent Decision Technologies, 12(1) pp.105-117.
     
  • Yakov Ben-Haim, Nicola M. Zetola and Clifford Dacso, 2012, Info-Gap Management of Public Health Policy for TB with HIV-Prevalence, BMC Public Health, 12: 1091. Pre-print. DOI: 10.1186/1471-2458-12-1091,
    URL: http://www.biomedcentral.com/1471-2458/12/1091
     
  • Kari Sentz and Francois Hemez, 2013, Information gap analysis for decision support systems in evidence-based medicine, International Conference on Machine Learning and Data Mining, Newark, NJ, July 22-24, 2013.
    Abstract

    Abstract

    The objective of evidence-based medicine is to come to well reasoned and justified clinical decisions regarding an individual patient’s case based on the integration of case-specific knowledge, medical expertise, and the best available clinical evidence. One significant challenge implicated in this pursuit stems from the volume of relevant information that can easily exceed what can reasonably be assessed. Thus intelligent systems that can mine and synthesize vast amounts of information would be invaluable. The reconciliation of such systems with the complexity and subtlety of decision support in medicine requires specialized capabilities. One untapped capability is furnished through the gap in information between what is known and what needs to be known to justify a decision. In this paper, we explore the value of an information gap analysis for robust decision-making in the context of evidence-based medicine with an eye to the potential role in automated evidence-based reasoning systems.

    Keywords

    Evidence-based medicine, robust decision making, informa- tion gap theory, deep question answering systems

  • Yakov Ben-Haim, Miriam Zacksenhouse, Carmit Keren and Clifford C. Dacso, 2009, Do We Know How to Set Decision Thresholds for Diabetes?  Medical Hypotheses, 73: 189-193.  Working paper.
     
  • Yakov Ben-Haim, Clifford C. Dacso, Jonathon Carrasco and Nithin Rajan, 2009, Heterogeneous Uncertainties in Cholesterol Management, International Journal of Approximate Reasoning, 50: 1046-1065.  Working paper.
     
  • Yakov Ben-Haim and Clifford C. Dacso, 2010, Info-gap decision theory and its potential applications in the clinic, an editorial, Personalized Medicine, vol. 7, #1, pp.1-3. Pre-publication version.
     
  • Diogo M. Souza Monteiro, L. Roman Carrasco, L. Joe Moffitt, Alasdair J.C. Cook, 2012, Robust surveillance of animal diseases: An application to the detection of bluetongue disease,Preventive Veterinary Medicine, 105: 17-24.
    Abstract

    Abstract

    Abstract Control of endemic, exotic, and emerging animal diseases critically depends on their early detection and timely management. This paper proposes a novel approach to evaluate alternative surveillance programs based on info-gap theory. A general modeling framework is developed explicitly accounting for severe uncertainty about the incursion, detection, spread, and control of exotic and emergent diseases. The model is illustrated by an evaluation of bluetongue disease surveillance strategies. Key results indicate that, when available, vaccination of the entire population is the most robust strategy. If vaccines are not available then active reporting of suspect clinical signs by farmers is a very robust surveillance policy.

    Keywords

    Surveillance; Exotic animal disease; Knightian uncertainty; Info-gap theory

  • Matthias C.M. Troffeas and John Paul Gosling, 2012, Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty, Intl J of Approximate Reasoning, 53: 1271-1281.
    Abstract

    Abstract

    When animals are transported and pass through customs, some of them may have dangerous infectious diseases. Typically, due to the cost of testing, not all animals are tested: a reasonable selection must be made. How to test effectively whilst avoiding costly disease outbreaks? First, we extend a model proposed in the literature for the detection of invasive species to suit our purpose, and we discuss the main sources of model uncertainty, many of which are hard to quantify. Secondly, we explore and compare three decision methodologies on the problem at hand, namely, Bayesian statistics, info-gap theory and imprecise probability theory, all of which are designed to handle severe uncertainty. We show that, under rather general conditions, every info-gap solution is maximal with respect to a suitably chosen imprecise probability model, and that therefore, perhaps surprisingly, the set of maximal options can be inferred at least partly – and sometimes entirely – from an info-gap analysis.

    Keywords

    Exotic disease, Lower prevision, Info-gap, Maximality, Minimax, Robustness

  • Matthias C.M. Troffeas and John Paul Gosling, Robust detection of exotic infectious diseases in animal herds: A comparative study of two decision methodologies under severe uncertainty, 7th International Symposium on Imprecise Probability: Theories and Applications, Innsbruck, Austria, 25-28 July 2011. Paper. Longer version.
     
  • Carmit Keren, 2009, Info-Gap Bayesian Classification, MSc Thesis, Technion-Israel Institute of Technology. In English.
     
  • Questions or comments on info-gap theory? Contact me at yakov@technion.ac.il