Díez's algorithm for the noisy MAX is very efficient polytrees, but when network has loops, it to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of conditional probability MAX, are not as polytrees can general algorithms such clustering or variable elimination, which more networks loops. In this article we propose new factorization that amounts in case and at same time than previous either elimination clustering. ©...

Bayesian networks (BNs) and influence diagrams (IDs) are probabilistic graphical models that widely used for building diagnosis- decision-support expert systems. Explanation of both the model reasoning is important debugging these models, alleviating users' reluctance to accept their advice, using them as tutoring This paper describes some explanation options BNs IDs have been implemented in Elvira how they medical teaching pre- postgraduate students.

A sum-product network (SPN) is a probabilistic model, based on rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal convex sums (weighted averages) products of functions. They are closely related to graphical models, particular Bayesian networks with multiple context-specific independencies. Their main advantage the possibility building tractable models from data, i.e., that can perform several inference tasks time proportional number...

Abstract Breast cancer is the most common in women. Ultrasound one of used techniques for diagnosis, but an expert field necessary to interpret test. Computer-aided diagnosis (CAD) systems aim help physicians during this process. Experts use Imaging-Reporting and Data System (BI-RADS) describe tumors according several features (shape, margin, orientation...) estimate their malignancy, with a language. To aid tumor BI-RADS explanations, paper presents deep neural network detection,...

Building probabilistic and decision-theoretic models requires a considerable knowledge engineering effort in which the most daunting task is obtaining numerical parameters. Authors of Bayesian networks usually combine various sources information, such as textbooks, statistical reports, databases, expert judgement. In this paper, we demonstrate risks combination, even when encompasses seemingly population-independent characteristics sensitivity specificity medical symptoms. We show that...

Non-small cell lung cancer (NSCLC) is the most prevalent type of and difficult to predict. When there are no distant metastases, optimal therapy depends mainly on whether malignant lymph nodes in mediastinum. Given vigorous debate among specialists about which tests should be used, our goal was determine sequence for each patient.

Objectives/Hypothesis To determine the incremental cost‐effectiveness of bilateral versus unilateral cochlear implantation for 1‐year‐old children suffering from sensorineural severe to profound hearing loss perspective Spanish public health system. Study Design Cost‐utility analysis. Methods We conducted a general‐population survey estimate quality‐of‐life increase contributed by second implant. built Markov influence diagram and evaluated it life‐long time horizon with 3% discount rate in...

Temporal Nodes Bayesian Networks (TNBNs) and of Probabilistic Events in Discrete Time (NPEDTs) are two different types Event (EBNs). Both based on the representation uncertain events, alternatively to Dynamic Networks, which deal with real-world dynamic properties. In a previous work, Arroyo-Figueroa Sucar applied TNBNs diagnosis prediction temporal faults that may occur steam generator fossil power plant. We present an NPEDT for same domain, along comparative evaluation networks. examine...

Summary Background: Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention worth economic cost. Decision trees, standard decision modeling technique for non-temporal domains, can only perform CEA very small problems. Objective: To develop a method problems involving several dozen variables. Methods: We explain how build influence diagrams (IDs) that explicitly represent cost and effectiveness. propose algorithm evaluating...

OpenMarkov is a Java open-source tool for creating and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, some Markov models. With more than 100,000 lines of code, it offers features interactive learning, explanation reasoning, cost-effectiveness analysis, which are not available in any other tool. has been used at universities, research centers, large companies 30 countries on four continents. Several them real-world medical applications, built with...

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends in the same way decision trees extend trees. They have been designed to build state-transition models, mainly medicine, and perform cost-effectiveness analyses. Using causal graph may contain several variables per cycle, MIDs can various patient characteristics without multiplying number states; particular, they represent history using tunnel states. OpenMarkov, an open-source tool, allows analyst...