Knowledge representation and diagnostic inference using Bayesian networks in the medical discourse

For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. This includes both generic and experience-based specific knowledge, which is stored in a knowledge base. For knowledge processing, a combination of the problem-solving methods of concept-based and case-based reasoning is used. Concept-based reasoning is used for the diagnosis, therapy and medication recommendation and evaluation of generic knowledge. Exceptions in the form of specific patient cases are processed by case-based reasoning. In addition, the use of Bayesian networks allows to deal with uncertainty, fuzziness and incompleteness. Thus, the valid general concepts can be issued according to their probability. To this end, various inference mechanisms are introduced and subsequently evaluated within the context of a developed prototype. Tests are employed to assess the classification of diagnoses by the network

Learning factor graphs in polynomial time and sample complexity

We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree can be learned in polynomial time and from a polynomial number of training examples, assuming that the data is generated by a network in this class. This result covers both parameter estimation for a known network structure and structure learning. It implies as a corollary that we can learn factor graphs for both Bayesian networks and Markov networks of bounded degree, in polynomial time and sample complexity. Importantly, unlike standard maximum likelihood estimation algorithms, our method does not require inference in the underlying network, and so applies to networks where inference is intractable. We also show that the error of our learned model degrades gracefully when the generating distribution is not a member of the target class of networks. In addition to our main result, we show that the sample complexity of parameter learning in graphical models has an O(1) dependence on the number of variables in the model when using the KL-divergence normalized by the number of variables as the performance criterion

Wissensrepräsentation und diagnostische Inferenz mittels Bayesscher Netze im medizinischen Diskursbereich

Für die diagnostische Inferenz unter Unsicherheit werden Bayessche Netze untersucht. Grundlage dafür bildet eine adäquate einheitliche Repräsentation des notwendigen Wissens. Dies ist sowohl generisches als auch auf Erfahrungen beruhendes spezifisches Wissen, welches in einer Wissensbasis gespeichert wird. Zur Wissensverarbeitung wird eine Kombination der Problemlösungsmethoden des Concept Based und Case Based Reasoning eingesetzt. Concept Based Reasoning wird für die Diagnose-, Therapie- und Medikationsempfehlung und -evaluierung über generischesWissen eingesetzt. Sonderfälle in Form von spezifischen Patientenfällen werden durch das Case Based Reasoning verarbeitet. Darüber hinaus erlaubt der Einsatz von Bayesschen Netze den Umgang mit Unsicherheit, Unschärfe und Unvollständigkeit. Es können so die gültigen allgemeinen Konzepte nach derenWahrscheinlichkeit ausgegeben werden. Dazu werden verschiedene Inferenzmechanismen vorgestellt und anschließend im Rahmen der Entwicklung eines Prototypen evaluiert. Mit Hilfe von Tests wird die Klassifizierung von Diagnosen durch das Netz bewertet.:1 Einleitung 2 Repräsentation und Inferenz 3 Inferenzmechanismen 4 Prototypische Softwarearchitektur 5 Evaluation 6 Zusammenfassun