Bayesian Networks in the Health Domain

2010 ◽  
pp. 342-376 ◽  
Author(s):  
Shyamala G. Nadathur
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Bayesian Networks ◽  
Network Model ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Large Datasets ◽  
Statistical Dependence ◽  
Health Domain ◽  
New Methods

Large datasets are regularly collected in biomedicine and healthcare (here referred to as the ‘health domain’). These datasets have some unique characteristics and problems. Therefore there is a need for methods which allow modelling in spite of the uniqueness of the datasets, capable of dealing with missing data, allow integrating data from various sources, explicitly indicate statistical dependence and independence and allow modelling with uncertainties. These requirements have given rise to an influx of new methods, especially from the fields of machine learning and probabilistic graphical models. In particular, Bayesian Networks (BNs), which are a type of graphical network model with directed links that offer a general and versatile approach to capturing and reasoning with uncertainty. In this chapter some background mathematics/statistics, description and relevant aspects of building the networks are given to better understand s and appreciate BN’s potential. There are also brief discussions of their applications, the unique value and the challenges of this modelling technique for the domain. As will be seen in this chapter, with the additional advantages the BNs can offer, it is not surprising that it is becoming an increasingly popular modelling tool in the health domain.

Reconciling internal and external satisfaction through probabilistic graphical models

2017 ◽  
Vol 9 (3/4) ◽  
pp. 347-370 ◽  
Author(s):  
Flaminia Musella ◽  
Roberta Guglielmetti Mugion ◽  
Hendry Raharjo ◽  
Laura Di Pietro
Keyword(s):  
Customer Satisfaction ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Object Oriented ◽  
Holistic Approach ◽  
Probabilistic Graphical Models ◽  
Model Parameters ◽  
Holistic View ◽  
Content Type ◽  
External Customer

Purpose This paper aims to holistically reconcile internal and external customer satisfaction using probabilistic graphical models. The models are useful not only in the identification of the most sensitive factors for the creation of both internal and external customer satisfaction but also in the generation of improvement scenarios in a probabilistic way. Design/methodology/approach Standard Bayesian networks and object-oriented Bayesian networks are used to build probabilistic graphical models for internal and external customers. For each ward, the model is used to evaluate satisfaction drivers by category, and scenarios for the improvement of overall satisfaction variables are developed. A global model that is based on an object-oriented network is modularly built to provide a holistic view of internal and external satisfaction. The linkage is created by building a global index of internal and external satisfaction based on a linear combination. The model parameters are derived from survey data from an Italian hospital. Findings The results that were achieved with the Bayesian networks are consistent with the results of previous research, and they were obtained by using a partial least squares path modelling tool. The variable ‘Experience’ is the most relevant internal factor for the improvement of overall patient satisfaction. To improve overall employee satisfaction, the variable ‘Product/service results’ is the most important. Finally, for a given target of overall internal and external satisfaction, external satisfaction is more sensitive to improvement than internal satisfaction. Originality/value The novelty of the paper lies in the efforts to link internal and external satisfaction based on a probabilistic expert system that can generate improvement scenarios. From an academic viewpoint, this study moves the service profit chain theory (Heskett et al., 1994) forward by delivering operational guidelines for jointly managing the factors that affect internal and external customer satisfaction in service organizations using a holistic approach.

scholarly journals Indirect Causes in Dynamic Bayesian Networks Revisited

2017 ◽  
Vol 59 ◽  
pp. 1-58
Author(s):  
Alexander Motzek ◽  
Ralf Möller
Keyword(s):  
Bayesian Networks ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Dynamic Bayesian Networks ◽  
Coarse Grained ◽  
Mathematical Basis ◽  
Use Of Time ◽  
Modeling Uncertainties ◽  
Spurious Results ◽  
Over Time

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a ``causal design,'' i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By identifying activator random variables, we propose activator dynamic Bayesian networks (ADBNs) which are able to rapidly adapt to contexts under a causal use of time, anticipating indirect influences on a solid mathematical basis using familiar Bayesian network semantics. ADBNs are well-defined dynamic probabilistic graphical models allowing one to model cyclic dependencies from local and causal perspectives while preserving a classical, familiar calculus and classically known algorithms, without introducing any overhead in modeling or inference.

scholarly journals Setting the Port Planning Parameters In Container Terminals through Bayesian Networks

2015 ◽  
Vol 27 (5) ◽  
pp. 395-403 ◽  
Author(s):  
Tomás Rodríguez García ◽  
Nicoletta González Cancelas ◽  
Francisco Soler-Flores
Keyword(s):  
Data Analysis ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Simulation Models ◽  
Probabilistic Graphical Models ◽  
Resource Planning ◽  
Container Terminals ◽  
Correct Prediction ◽  
Port Planning ◽  
Transport Logistics

The correct prediction in the transport logistics has vital importance in the adequate means and resource planning and in their optimisation. Up to this date, port planning studies were based mainly on empirical, analytical or simulation models. This paper deals with the possible use of Bayesian networks in port planning. The methodology indicates the work scenario and how the network was built. The network was afterwards used in container terminals planning, with the support provided by the tools of the Elvira code. The main variables were defined and virtual scenarios inferences were realised in order to carry out the analysis of the container terminals scenarios through probabilistic graphical models. Having performed the data analysis on the different terminals and on the considered variables (berth, area, TEU, crane number), the results show the possible relationships between them. Finally, the conclusions show the obtained values on each considered scenario.

Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children

2019 ◽  
Vol 1 ◽  
pp. 118-125
Author(s):  
W. Łaguna ◽  
J. Bagińska ◽  
A. Oniśko
Keyword(s):  
Risk Factors ◽  
Dental Caries ◽  
Bayesian Network ◽  
Network Model ◽  
Graphical Models ◽  
Network Modeling ◽  
Significant Risk ◽  
Probabilistic Graphical Models ◽  
Bayesian Network Model ◽  
Bayesian Network Modeling

<br/><b>Purpose</b> - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. <br/><b>Materials and Methods</b> - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. <br/><b>Results</b> - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. <br/><b>Conclusions</b> - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.

scholarly journals Is Domain Knowledge Necessary for Machine Learning Materials Properties?

2020 ◽  
Author(s):  
Ryan Murdock ◽  
Steven Kauwe ◽  
Anthony Wang ◽  
Taylor Sparks
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Predictive Ability ◽  
Large Datasets ◽  
Comparative Efficacy ◽  
Learning Materials ◽  
Material Informatics ◽  
Materials Properties ◽  
New Methods

<div>New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple one-hot encoding of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or data that is not fully representative we show that domain knowledge offers advantages in predictive ability.</div><div><br></div>

scholarly journals Is Domain Knowledge Necessary for Machine Learning Materials Properties?

2020 ◽  
Author(s):  
Ryan Murdock ◽  
Steven Kauwe ◽  
Anthony Wang ◽  
Taylor Sparks
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Predictive Ability ◽  
Large Datasets ◽  
Comparative Efficacy ◽  
Learning Materials ◽  
Material Informatics ◽  
Materials Properties ◽  
New Methods

<div>New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple one-hot encoding of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or data that is not fully representative we show that domain knowledge offers advantages in predictive ability.</div><div><br></div>

scholarly journals Probabilistic Ranking Of Microbiomes Plus Taxa Selection to discover and validate microbiome function models for multiple litter decomposition studies

2020 ◽  
Author(s):  
Jaron Thompson ◽  
Nicholas Lubbers ◽  
Marie E. Kroeger ◽  
Rae DeVan ◽  
Renee Johansen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Litter Decomposition ◽  
Network Model ◽  
Relative Abundance ◽  
Learning Approaches ◽  
Bayesian Network Model

AbstractThe overwhelming complexity of microbiomes makes it difficult to decipher functional relationships between specific microbes and ecosystem properties. While machine learning analyses have demonstrated an impressive ability to correlate microbial community composition with macroscopic functions, mechanisms that dictate model predictions are often unknown, and predictions often lack an assigned metric of uncertainty. In this study, we apply Bayesian networks to build on prior feature selection analyses and construct easy-to-interpret probabilistic models, which accurately predict levels of dissolved organic carbon (DOC) from the relative abundance of soil bacteria (16S rRNA gene profiles). In addition to standard cross-validation, we show that a Bayesian network model trained using samples from a pine litter decomposition study accurately predicts DOC of samples from an independent oak litter decomposition study, suggesting that mechanisms driving variation in soil carbon storage may be conserved across different types of decomposing plant litter. Furthermore, the structure of the resulting Bayesian network model defines a minimal set of highly informative taxa, whose abundances directly constrain the probability of high or low DOC conditions. Significant accuracy of the Bayesian network model with independent data sets supports the validity of the identified relationships between taxa abundance and DOC.SummaryUnderstanding the interplay between microbiomes and the environments they inhabit is a daunting task. While recent advances in gene sequencing technology provide a means of profiling the relative abundance of microbial species, the resulting data are noisy, sparse, and limited to small sample sizes. Despite these challenges, machine learning approaches have demonstrated a promising ability to discover patterns linking the microbiome with macroscopic behavior. However, most machine learning models applied to microbiome data do not estimate prediction uncertainty and provide little insight regarding how predictions are made. In this study, we couple machine learning approaches for feature reduction with Bayesian networks to model the relationship between the soil microbiome and dissolved organic carbon (DOC). We show that Bayesian networks are accurate and provide a transparent link between microbial abundance and DOC. To validate Bayesian networks, we demonstrate accurate predictions for held-out testing data and with data from independent decomposition experiments.

scholarly journals Graphical Models in Reconstructability Analysis and Bayesian Networks

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 986
Author(s):  
Marcus Harris ◽  
Martin Zwick
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Graphical Models ◽  
System Structure ◽  
Neutral System ◽  
Graphical Modeling ◽  
Directed System ◽  
System Variables

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and BN by developing and visualizing: (1) a BN neutral system lattice of general and specific graphs, (2) a joint RA-BN neutral system lattice of general and specific graphs, (3) an augmented RA directed system lattice of prediction graphs, and (4) a BN directed system lattice of prediction graphs. Additionally, it (5) extends RA notation to encompass BN graphs and (6) offers an algorithm to search the joint RA-BN neutral system lattice to find the best representation of system structure from underlying system variables. All lattices shown in this paper are for four variables, but the theory and methodology presented in this paper are general and apply to any number of variables. These methodological innovations are contributions to machine learning and artificial intelligence and more generally to complex systems analysis. The paper also reviews some relevant prior work of others so that the innovations offered here can be understood in a self-contained way within the context of this paper.

Introduction to Bayesian Networks and Influence Diagrams

2012 ◽  
pp. 9-32
Author(s):  
Luis Enrique Sucar
Keyword(s):  
Probability Theory ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Dynamic Bayesian Networks ◽  
Influence Diagrams ◽  
General Description ◽  
Sequential Decision ◽  
The Core ◽  
Inference Techniques

In this chapter we will cover the fundamentals of probabilistic graphical models, in particular Bayesian networks and influence diagrams, which are the basis for some of the techniques and applications that are described in the rest of the book. First we will give a general introduction to probabilistic graphical models, including the motivation for using these models, and a brief history and general description of the main types of models. We will also include a brief review of the basis of probability theory. The core of the chapter will be the next three sections devoted to: (i) Bayesian networks, (ii) Dynamic Bayesian networks and (iii) Influence diagrams. For each we will introduce the models, their properties and give some examples. We will briefly describe the main inference techniques for the three types of models. For Bayesian and dynamic Bayesian nets we will talk about learning, including structure and parameter learning, describing the main types of approaches. At the end of the section on influence diagrams we will briefly introduce sequential decision problems as a link to the chapter on MDPs and POMDPs. We conclude the chapter with a summary and pointers for further reading for each topic.

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