CO-AUTHORS Prof & HOD Dr.I.Chandrasekaran M.D.,D.A., Prof Dr.S.P.Meenakshisundaram M.D.,D.A.,
Dr. Anthony Constantinou , Prof. Norman...
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[1 in 1,000 people have disease D. A test for disease D is 100% accurate for patients who have the disease and 95% accurate for those who don`t. ] Determining whether a decision (such as which type of investment to make) requires further information and what is the value of getting further information prior to making a decision [4]. Improved decision support with respect to whether a mentally ill patient is determined suitable for discharge on the basis of simulating relevant interventions and assessing the individual's risk of violence [5]. Dr. Anthony Constantinou a , Prof. Norman Fenton b School of Electronic Engineering and Computer Science, Queen Mary University of London. a) E-mail: [email protected] , Web: www.constantinou.info b) E-mail: [email protected] , Web: www.eecs.qmul.ac.uk/~norman/ I can provide expert information for factors which data fails to capture, but I may be biased. They expect so much from me, but even when I am really big, I may be limited, unstructured, or even flawed. Given that a previous patient died from disease D, what would be the probability for death had we also known information X and on this basis taken action Y. Counterfactual Interventions Risk Decisions Having taken everything into consideration, which sets of test and treatment optimise the patient’s outcome ? What is the probability a patient with disease D will improve if given treatment T ? What is the risk the patient gets disease D in the next 10 years ? SET for BRITAIN, 2016 Inverse inference My patient received a positive test result. What is the probability she has disease D ? Improved predictive accuracy with respect to whether a prisoner is determined suitable for release on the basis of risk management of the individual's risk of reoffending [1]. Consistent profitability against published market odds by predicting the outcome of football matches and simulating bets against the gambling market [2, 3]. [1] Constantinou, A. C., Freestone, M., Marsh W., Fenton, N., & Coid, J.W. (2015). Risk assessment and risk management of violent reoffending among prisoners. Expert Systems with Applications , 42(21): 7511-7529. [2] Constantinou, A. C., Fenton, N. E. & Neil, M. (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Knowledge-Based Systems, 36: 322, 339. [3] Constantinou, A. C., Fenton, N. E. & Neil, M. (2013). Profiting from an inefficient Association Football gambling market: Prediction, Risk and Uncertainty using Bayesian networks. Knowledge-Based Systems, 50: 60-86. [4] Constantinou, A. C., Yet, B., Fenton, N., Neil, M., & Marsh, W. (2016). Value of Information Analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences. Artificial Intelligence in Medicine, 66: 41-52. [5] Constantinou, A. C., Freestone, M., Marsh, W., & Coid, J. (2015). Causal inference for violence risk management and decision support in Forensic Psychiatry. Decision Support Systems , 80: 42-55. The answer is 2%. Most people, including me, get it wrong. In fact Casscells et al., showed that only 18% of participants (staff and students) at the Harvard Medical School gave the correct answer. We acknowledge funding support by the European Research Council (ERC), EU grant ERC-2013-AdG339182-BAYES_KNOWLEDGE, by the Engineering and Physical Sciences Research Council (EPSRC), and also by a Program Grant for Applied Research, program RP-PG-0407- 10500, from The National Institute for Health Research UK (NIHR). Some of the clipart used in this poster is provided for free by Freepik.com and www.flaticon.com, licensed by Creative Commons BY 3.0, CC 3.0 BY.
Transcript of Dr. Anthony Constantinou , Prof. Norman...
[1 in 1,000 people have diseaseD. A test for disease D is 100%accurate for patients who havethe disease and 95% accuratefor those who don`t. ]
Determining whether a decision (such as which type ofinvestment to make) requires further information andwhat is the value of getting further information prior tomaking a decision [4].
Improved decision support with respect to whether amentally ill patient is determined suitable for dischargeon the basis of simulating relevant interventions andassessing the individual's risk of violence [5].
Dr. Anthony Constantinoua, Prof. Norman Fentonb
School of Electronic Engineering and Computer Science, Queen Mary University of London.a) E-mail: [email protected], Web: www.constantinou.infob) E-mail: [email protected], Web: www.eecs.qmul.ac.uk/~norman/
I can provide expert
information for factors which data
fails to capture, but I may be
biased.
They expect so much from me, but
even when I am really big, I may be
limited, unstructured, or
even flawed.
Given thata previous patient
died from disease D,what would be the
probability for death had we also known
information X andon this basis taken
action Y.
CounterfactualInterventionsRisk Decisions
Having taken everything into
consideration, which sets of test and
treatment optimise the patient’s
outcome ?
What is the probability a patient with
disease D will improve if given
treatment T ?
What is the risk the patient gets disease Din the next 10
years ?
SET for BRITAIN, 2016
Inverse inference
My patient received a positive test result. What is the probability she
has disease D ?
Improved predictive accuracy with respectto whether a prisoner is determinedsuitable for release on the basis of riskmanagement of the individual's risk ofreoffending [1].
Consistent profitability against publishedmarket odds by predicting the outcome offootball matches and simulating betsagainst the gambling market [2, 3].
[1] Constantinou, A. C., Freestone, M., Marsh W., Fenton, N., & Coid, J.W. (2015). Risk assessment and risk management of violent reoffending among prisoners. Expert Systems with Applications, 42(21): 7511-7529.
[2] Constantinou, A. C., Fenton, N. E. & Neil, M. (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Knowledge-Based Systems, 36: 322, 339.
[3] Constantinou, A. C., Fenton, N. E. & Neil, M. (2013). Profiting from an inefficient Association Football gambling market: Prediction, Risk and Uncertainty using Bayesian networks. Knowledge-Based Systems, 50: 60-86.
[4] Constantinou, A. C., Yet, B., Fenton, N., Neil, M., & Marsh, W. (2016). Value of Information Analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences. Artificial Intelligence in Medicine, 66: 41-52.
[5] Constantinou, A. C., Freestone, M., Marsh, W., & Coid, J. (2015). Causal inference for violence risk management and decision support in Forensic Psychiatry. Decision Support Systems, 80: 42-55.
The answer is 2%. Mostpeople, including me, getit wrong. In fact Casscellset al., showed that only18% of participants (staffand students) at theHarvard Medical Schoolgave the correct answer.
We acknowledge funding support by the European Research Council(ERC), EU grant ERC-2013-AdG339182-BAYES_KNOWLEDGE, by theEngineering and Physical Sciences Research Council (EPSRC), andalso by a Program Grant for Applied Research, program RP-PG-0407-10500, from The National Institute for Health Research UK (NIHR). Someof the clipart used in this poster is provided for free by Freepik.com andwww.flaticon.com, licensed by Creative Commons BY 3.0, CC 3.0 BY.
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