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Bayesian Networks and Decision Graphs: Inference and Application in Medical Scenarios

This article delves into the principles of Bayesian networks as introduced by F. V. Jensen. It covers essential concepts from probability theory to modeling and learning Bayesian networks. Key topics include inference techniques like variable elimination and junction trees, with practical applications in medical scenarios such as patient conditions monitoring (e.g., hypovolemia, anaphylaxis) and decision support systems. This insightful exploration emphasizes the significance of graphical models in decision-making processes and their utility in clinical settings.

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Bayesian Networks and Decision Graphs: Inference and Application in Medical Scenarios

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  1. MINVOLSET KINKEDTUBE PULMEMBOLUS INTUBATION VENTMACH DISCONNECT PAP SHUNT VENTLUNG VENITUBE PRESS MINOVL FIO2 VENTALV PVSAT ANAPHYLAXIS ARTCO2 EXPCO2 SAO2 TPR INSUFFANESTH HYPOVOLEMIA LVFAILURE CATECHOL LVEDVOLUME STROEVOLUME ERRCAUTER HR ERRBLOWOUTPUT HISTORY CO CVP PCWP HREKG HRSAT HRBP BP Mainly based on F. V. Jensen, „Bayesian Networks and Decision Graphs“, Springer-Verlag New York, 2001. Advanced IWS 06/07 Graphical Models- Inference - Most Probable Explanation Wolfram Burgard, Luc De Raedt, Kristian Kersting, Bernhard Nebel Albert-Ludwigs University Freiburg, Germany

  2. Outline • Introduction • Reminder: Probability theory • Basics of Bayesian Networks • Modeling Bayesian networks • Inference (VE, Junction tree,MPE) • Excourse: Markov Networks • Learning Bayesian networks • Relational Models

  3. Elimination operator factor B: P(b|a) P(d|b,a) P(e|b,c) B facotr C: P(c|a) C factor D: D factor E: e=0 E factor A: P(a) A P(a|e=0) VE, Bucket elimination [Dechter ‘96] - Inference (MPE)

  4. factor B: P(b|a) P(d|b,a) P(e|b,c) B factor C: P(c|a) C factor D: D factor E: e=0 E P(a) factor A: A MPE Finding MPE [Dechter ‘96] Elimination operator - Inference (MPE)

  5. B: P(b|a) P(d|b,a) P(e|b,c) C: P(c|a) D: E: e=0 A: P(a) Generating the MPE-tuple - Inference (MPE)

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