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en:dydaktyka:problog:lab1 [2017/05/30 09:13]
msl [Learning + Priors]
en:dydaktyka:problog:lab1 [2019/06/27 15:49] (current)
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-====== Probabilistic Programming ​--- Medical Cases ======+====== Probabilistic Programming ​— Diagnosis and Prediction ​======
  
 This class will cover use cases of the Bayesian methods in the medical domain. First part of the class is based on article: "Local computations with probabilities on graphical structures and their application to expert systems"​ by Lauritzen, Steffen L. and David J. Spiegelhalter. Second part is inspired by "An intercausal cancellation model for bayesian-network engineering. International Journal of Approximate Reasoning"​ by S.P. Woudenberg, L. C. van der Gaag, and C. M. Rademaker. This class will cover use cases of the Bayesian methods in the medical domain. First part of the class is based on article: "Local computations with probabilities on graphical structures and their application to expert systems"​ by Lauritzen, Steffen L. and David J. Spiegelhalter. Second part is inspired by "An intercausal cancellation model for bayesian-network engineering. International Journal of Approximate Reasoning"​ by S.P. Woudenberg, L. C. van der Gaag, and C. M. Rademaker.
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       * logical or in Problog: <code prolog>​a_or_b :- a.        * logical or in Problog: <code prolog>​a_or_b :- a. 
 a_or_b :- b. </​code>​ a_or_b :- b. </​code>​
-      * you can have lung cancer even if you are not smoker, so you should add include in model something similar ​to the line belowAnalogically other diseases ;) <code prolog>​0.01::​smoker.</​code>  ​+      * you can have lung cancer even if you are not smoker, so don't forget ​to mention this fact in the modelThere are two ways to do that: 
 +        * classical (Bayesian network):<code prolog>​0.1::​cancer :- smoker. 
 +0.01::​cancer :- \+ smoker.</​code>​  
 +          * pros: it's a classical approach with clean Bayesian semantics 
 +          * cons: including new variables into the model can grow number of rules exponentially;​ also it may be required to modify already existing rules 
 +        * Problog '​additive way':<​code prolog>​0.1::​cancer :- smoker. 
 +0.01::​cancer.</​code>  ​ 
 +          * pros: including new variable doesn'​t involve change of the old rules; you just add new rule per variable 
 +          * cons: the probabilities will be different than in the Bayesian network; so you can't just copy the Bayesian network structure ;)
       * x-ray can be positive even if you're healthy       * x-ray can be positive even if you're healthy
  
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   - write a corresponding Problog program:   - write a corresponding Problog program:
     * you may have to introduce additional variables for every kind of treatment to indicate if the treatment is inhibited, e.g. calcium effects in **something** that treats the osteoporosis     * you may have to introduce additional variables for every kind of treatment to indicate if the treatment is inhibited, e.g. calcium effects in **something** that treats the osteoporosis
-  - what is the chance of successful treatment when we use both calcium and bisphosphonates+  - what is the chance of successful treatment when we use both calcium and bisphosphonates?
en/dydaktyka/problog/lab1.1496128421.txt.gz · Last modified: 2019/06/27 16:00 (external edit)
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