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At least 91 records · Page 5Linked to original sources
PubMed2005

Using Bayesian networks to predict survival of liver transplant patients.

The relative scarcity of grafts available for liver transplantation highlights the need to identify patients likely to have good outcomes after treatment. We used transplant information from the United Network for Organ Sharing database to construct a Bayesian network model to predict 90-day graft survival. The final model incorporated a set of 29 pre-transplant variables, and it achieved performance, as measured by area under the receiver operating characteristic curve, of 0.674 by cross-validation and 0.681 on an independent validation set. The results showed a positive predictive value of 91%, while the negative predictive value was lower at 30%. With additional refinement and validation, our model may be useful as an adjunct to clinical experience in identifying patients most likely to have good outcomes following liver transplantation.

Adult↗
PubMed1993

Drug delivery optimization through Bayesian networks: an application to erythropoietin therapy in uremic anemia.

This paper describes how Bayesian networks can be used in combination with compartmental models to plan recombinant human erythropoietin delivery in the treatment of anemia of chronic uremic patients. Past measurements of hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of erythropoiesis. This adaptive process provides more accurate patient-specific predictions, and hence a more rational dosage planning. Inferences are performed by using a stochastic simulation algorithm called Gibbs sampling. We describe a drug delivery optimization protocol based on our approach. Some results obtained on real data are presented.

Adult↗
PubMed2005

Bayesian networks and probabilistic reasoning about scientific evidence when there is a lack of data.

Bayesian networks (BNs) are a kind of graphical model that formally combines elements of graph and probability theory. BNs are a mathematically and statistically rigorous technique allowing their user to define a pictorial representation of assumed dependencies and influences among a set of variables deemed to be relevant for a particular inferential problem. The formalism allows one to process newly acquired evidence according to the rules of probability calculus. Applications of BNs have been reported in various forensic disciplines. However, there seems to be some reluctance to consider BNs as a more general framework for representing and evaluating sources of uncertainties associated with scientific evidence. Notably, BNs are widely thought of as an essentially numerical method, requiring "exact" numbers with a high "accuracy". The present paper aims to draw the reader's attention to the point that the availability of hard numerical data is not a necessary requirement for using BNs in forensic science. An abstraction of quantitative BNs, known as qualitative probabilistic networks (QPNs), and sensitivity analyses are presented and their potential applications discussed. As a main difference to their quantitative counterpart, QPNs contain qualitative probabilistic relationships instead of numerical relations. Sensitivity analyses consist of varying the probabilities assigned to one or more variables and evaluating the effect on one or more other variables of interest. Both QPNs and sensitivity analyses appear to be useful concepts that permit one to work in contexts with acute lack of numerical data and where reasoning consistent with the laws of probability should nevertheless be performed.

Journal Article↗
PubMed2005

Using Bayesian networks to model expected and unexpected operational losses.

This report describes the use of Bayesian networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its focus is on modeling "long" tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying controls process. The use of causal modeling is discussed from the perspective of exploiting local expertise about process reliability and formally connecting this knowledge to actual or hypothetical statistical phenomena resulting from the process. This brings the benefit of supplementing sparse data with expert judgment and transforming qualitative knowledge about the process into quantitative predictions. We conclude that BNs can help combine qualitative data from experts and quantitative data from historical loss databases in a principled way and as such they go some way in meeting the requirements of the draft Basel II Accord (Basel, 2004) for an advanced measurement approach (AMA).

Journal Article↗
PubMed2005

Causal Bayesian network for tagging syntactical structure of Croatian sentences.

Paper describes tagging syntactical structure of Croatian language sentences using causal Bayesian network. In the first part of the paper we describe Bayesian model for tagging sentences. Base on this idea, we will test our model on Croatian language sentences on Database of grammatical sentences of Croatian language (http://infoz.ffzg.hr / tepes /). This paper is result of our new research connected with the paper hidden Markov model for tagging of Croatian language texts for project Linguistic Analysis of The European languages and the paper Probability distribution on the parse trees for the project Annotated database and syntactic structure of Croatian languages.

Anthropology↗
PubMed2005

A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients.

We developed a Bayesian network coding scheme for annotating biomedical content in layperson-oriented clinical genetics documents. The coding scheme supports the representation of probabilistic and causal relationships among concepts in this domain, at a high enough level of abstraction to capture commonalities among genetic processes and their relationship to health. We are using the coding scheme to annotate a corpus of genetic counseling patient letters as part of the requirements analysis and knowledge acquisition phase of a natural language generation project. This paper describes the coding scheme and presents an evaluation of intercoder reliability for its tag set. In addition to giving examples of use of the coding scheme for analysis of discourse and linguistic features in this genre, we suggest other uses for it in analysis of layperson-oriented text and dialogue in medical communication.

Artificial Intelligence↗
PubMed2006

Inference in multiply sectioned Bayesian networks: methods and performance comparison.

This paper extends lazy propagation for inference in single-agent Bayesian networks (BNs) to multiagent lazy inference in multiply sectioned BNs (MSBNs). Two methods are proposed using distinct runtime structures. It was proved that the new methods are exact and efficient when the domain structure is sparse. Both improve space and time complexity more than the existing method, which allows multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. The relative performances of the three methods are compared analytically and experimentally.

Algorithms↗
PubMed2003

A Bayesian networks approach for predicting protein-protein interactions from genomic data.

We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

Bayes Theorem↗
PubMed2026

Suction-assisted ureteroscopy compared with traditional ureteroscopy for renal stones ≤ 2 cm: a systematic review, Bayesian network meta-analysis and meta-regression.

INTRODUCTION AND OBJECTIVE: Suction-enhanced flexible ureteroscopy (URS) aims to improve stone clearance and reduce complications. We performed a Bayesian network meta-analysis to compare the efficacy and safety of flexible aspiration navigable sheaths (FANS) and direct in-scope suction (DISS) for renal calculi ≤ 2 cm. METHODS: A systematic search of PubMed, MEDLINE, Scopus, Web of Science, and Google Scholar was conducted through June 2026. Comparative studies of FANS, DISS, or conventional access sheaths for renal stones ≤ 2 cm were included. The primary outcome was 30-day stone-free rate (SFR). Secondary outcomes included operative time, fever, sepsis, and complications. A Bayesian random-effects network meta-analysis synthesized direct and indirect evidence. RESULTS: Seventeen studies including 3,657 patients (1,677 FANS, 56 DISS, 1,924 control) were included. FANS showed higher SFR (OR 2.5, 95% CrI 2.0-3.1), while grouped DISS had a similar but less precise effect (OR 3.1, 95% CrI 1.0-8.8). Calyxo V2 had the highest SFR (OR 5.4, 95% CrI 1.0-29.0), whereas PUSEN showed no significant difference (OR 1.6, 95% CrI 0.41-6.3). FANS reduced postoperative fever and complications. FANS also showed lower odds of postoperative sepsis (OR 0.40, 95% CrI 0.12-0.97). CONCLUSIONS: Suction-assisted ureteroscopy improves SFR for renal calculi ≤ 2 cm. FANS was associated with shorter operative time, fever, and complications. DISS systems show promising but limited results, with performance differing by technology configuration. Larger prospective trials are needed.

Humans↗
PubMed2006

Object-oriented Bayesian networks for complex forensic DNA profiling problems.

We describe a flexible computational toolkit, based on object-oriented Bayesian networks, that can be used to model and solve a wide variety of complex problems of relationship testing using DNA profiles. In particular this can account for such complicating features as missing individuals, mutation and null alleles. We illustrate the use of this toolkit with several examples, including disputed paternity with missing or additional measurements, and criminal identification. We investigate the effects on likelihood ratios of introducing mutation and/or null alleles, and show that this can be substantial even when the underlying perturbations are small.

Bayes Theorem↗
PubMed2005

On the relationship between deterministic and probabilistic directed Graphical models: from Bayesian networks to recursive neural networks.

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directed acyclic graph. In BNs, the conditional relationships between parent and child variables are probabilistic, whereas in RNNs they are deterministic and parameterized by neural networks. Here, we study the formal relationship between both classes of models and show that when the source nodes variables are observed, RNNs can be viewed as limits, both in distribution and probability, of BNs with local conditional distributions that have vanishing covariance matrices and converge to delta functions. Conditions for uniform convergence are also given together with an analysis of the behavior and exactness of Belief Propagation (BP) in 'deterministic' BNs. Implications for the design of mixed architectures and the corresponding inference algorithms are briefly discussed.

Bayes Theorem↗
PubMed2006

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Algorithms↗
PubMed2005

Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks.

We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or ORFs (Open Reading Frames) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore many functional links would be missing when only one or two source of data is used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

Artificial Intelligence↗
PubMed2001

Support of diagnosis of liver disorders based on a causal Bayesian network model.

We describe our work on HEPAR II, a probabilistic causal model for diagnosis of liver disorders. The model, a Bayesian network capturing the causal interactions among various risk factors, diseases, symptoms, and test results, is based on expert knowledge combined with clinical data captured in medical records. The main applications of HEPAR II are assistance is diagnosis and training of beginning diagnosticians. We outline the principles of the applied approach, present a brief description of the model, and report its diagnostic performance.

Algorithms↗
PubMed1998

Guideline generation from data by induction of decision tables using a Bayesian network framework.

Decision tables can be used to represent practice guidelines effectively. In this study we adopt the powerful probabilistic framework of Bayesian Networks (BN) for the induction of decision tables. We discuss the simplest BN model, the Naive Bayes and extend it to the Two-Stage Naive Bayes. We show that reversal of edges in Naive Bayes and Two-stage Naive Bayes results in simple decision table and hierarchical decision table respectively. We induce these graphical models for dementia severity staging using the Clinical Dementia Rating Scale (CDRS) database from the University of California, Irvine, Alzheimer's Disease Research Center. These induced models capture the two-stage methodology clinicians use in computing the global CDR score by first computing the six category scores of memory, orientation, judgment and problem solving, community affairs, home and hobbies and personal care, and then the global CDRS. The induced Two-Stage models also attain a clinically acceptable performance when compared to domain experts and could serve as useful guidelines for dementia severity staging.

Algorithms↗
PubMed2002

Bayesian network approach to cell signaling pathway modeling.

The modeling of cellular signaling pathways is an emerging field. Sachs et al. illustrate the application of Bayesian networks to an example cellular pathway involving the activation of focal adhesion kinase (FAK) and extracellular signal-regulated kinase (ERK) in response to fibronectin binding to an integrin. They describe how to use the analysis to select from among proposed models, formulate hypotheses regarding component interactions, and uncover potential dynamic changes in the interactions between these components. Although the data sets currently available for this example problem are too small to definitively point to a particular model, the approach and results provide a glimpse into the power that these methods will achieve once the technology for obtaining the necessary data becomes readily available.

Bayes Theorem↗
PubMed2006

Joint learning of gene functions--a Bayesian network model approach.

In this paper, we develop a machine learning system for determining gene functions from heterogeneous data sources using a Weighted Naive Bayesian network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or Open Reading Frames (ORFs) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore, many functional links would be missing when only one or two sources of data are used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations, and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

Artificial Intelligence↗
PubMed2006

Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.

PURPOSE: To retrospectively determine whether a Bayesian network (BN) computer model can accurately predict the probability of breast cancer on the basis of risk factors and mammographic appearance of microcalcifications, to improve the positive predictive value (PPV) of biopsy, with pathologic examination and follow-up as reference standards. MATERIALS AND METHODS: The institutional review board approved this HIPAA-compliant study; informed consent was not required. Results of 111 consecutive image-guided breast biopsies performed for microcalcifications deemed suspicious by radiologists were analyzed. Mammograms obtained before biopsy were analyzed in a blinded manner by a breast imager who recorded Breast Imaging Reporting and Data System (BI-RADS) descriptors and provided a probability of malignancy. The BN uses probabilistic relationships between breast disease and mammography findings to estimate the risk of malignancy. Probability estimates from the radiologist and the BN were used to create receiver operating characteristic (ROC) curves, and area under the ROC curve (A(z)) values were compared. PPV of biopsy was also evaluated on the basis of these probability estimates. RESULTS: The BN and the radiologist achieved A(z) values of 0.919 and 0.916, respectively, which were not significantly different. If the 34 patients estimated by the BN to have less than a 10% probability of malignancy had not undergone biopsy, the PPV of biopsy would have increased from 21.6% to 31.2% without missing a breast cancer (P < .001). At this level, the radiologist's probability estimation improved the PPV to 30.0% (P < .001). CONCLUSION: A probabilistic model that includes BI-RADS descriptors for microcalcifications can distinguish between benign and malignant abnormalities at mammography as well as a breast imaging specialist can and may be able to improve the PPV of image-guided breast biopsy.

Adult↗