Incorporating prior expert knowledge in learning Bayesian networks from genetic epidemiological data

2014 ◽  
Author(s):  
Chengwei Su ◽  
Mark E. Borsuk ◽  
Angeline Andrew ◽  
Margaret Karagas
Keyword(s):  
Bayesian Networks ◽  
Expert Knowledge ◽  
Epidemiological Data

ANOMALY DETECTION IN VESSEL TRACKING – A BAYESIAN NETWORKS (BNs) APPROACH

2021 ◽  
Vol 157 (A3) ◽  
Author(s):  
D Handayani ◽  
W Sediono ◽  
A Shah
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Expert Knowledge ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Identification System ◽  
International Convention ◽  
Learning Techniques ◽  
Reporting Data ◽  
Vessel Anomaly

The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.

The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale

2015 ◽  
Vol 66 (5) ◽  
pp. 930-941 ◽  
Author(s):  
K. Taalab ◽  
R. Corstanje ◽  
T. M. Mayr ◽  
M. J. Whelan ◽  
R. E. Creamer
Keyword(s):  
Bayesian Networks ◽  
Bulk Density ◽  
Expert Knowledge ◽  
Soil Bulk Density ◽  
Landscape Scale

scholarly journals Determination of the Type of Heart Syndrome in Traditional Chinese Medicine with the Bayesian Network Method

2020 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Asto Buditjahjanto
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Expert Knowledge ◽  
Prior Probabilities ◽  
Network Method

The determination of a disease syndrome in the TCM is difficult enough to determine because it requires a lot of experience in observing patients' symptoms that appear in disease syndrome and their disease syndrome history. Symptoms that appear in one disease syndrome are varied and can also appear in other disease syndromes. This research limits the determination of the type of syndrome only in the heart organ. The purpose of this study is to determine the type of heart syndrome in TCM by using Bayesian Networks. Bayesian Networks is used because it has the advantage of adapting expert knowledge toward the preferences of symptoms that arise at a type of heart syndrome. The expert's preference is in the weights that act as prior probabilities that are used as the basis for calculations on the Bayesian Networks. The results showed that the Bayesian Networks can be used to determine the type of heart syndrome well. The results of trials on 7 patients yield the same diagnosis between the doctor's diagnosis and the Bayesian Networks calculation

scholarly journals Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model

2019 ◽  
Vol 11 (18) ◽  
pp. 5021 ◽  
Author(s):  
Zhaobo Chen ◽  
Gangzhu Qiao ◽  
Jianchao Zeng
Keyword(s):  
Bayesian Networks ◽  
Coal Mine ◽  
Expert Knowledge ◽  
Assessment Method ◽  
Mine Safety ◽  
Contributory Factor ◽  
Fitness For Duty ◽  
Mine Accidents ◽  
The Relationship

Unsafe behaviours, such as violations of rules and procedures, are commonly identified as important causal factors in coal mine accidents. Meanwhile, a recurring conclusion of accident investigations is that worker states, such as mental fatigue, illness, physiological fatigue, etc., are important contributory factors to unsafe behaviour. In this article, we seek to provide a quantitative analysis on the relationship between the worker state and unsafe behaviours in coal mine accidents, based on a case study drawn from Chinese practice. Using Bayesian networks (BN), a graphical structure of the network was designed with the help of three experts from a coal mine safety bureau. In particular, we propose a verbal versus numerical fuzzy probability assessment method to elicit the conditional probability of the Bayesian network. The junction tree algorithm is further employed to accomplish this analysis. According to the BN established by expert knowledge, the results show that when the worker is in a poor state, the most vulnerable unsafe behaviour is violation, followed by decision-making error. Furthermore, insufficient experience may be the most significant contributory factor to unsafe behaviour, and poor fitness for duty may be the principal state that causes unsafe behaviours.

scholarly journals A Bayesian approach to ecosystem service trade-off analysis utilizing expert knowledge

2019 ◽  
Vol 40 (1) ◽  
pp. 67-83 ◽  
Author(s):  
Solveig Höfer ◽  
Alex Ziemba ◽  
Ghada El Serafy
Keyword(s):  
Ecosystem Services ◽  
Bayesian Networks ◽  
Ecosystem Service ◽  
Expert Knowledge ◽  
Expert Elicitation ◽  
Service Trade ◽  
Multiple Sources ◽  
Sustainable Resource Management ◽  
Promising Tool ◽  
Trade Offs

Abstract The concept of ecosystem services is gaining attention in the context of sustainable resource management. However, it is inherently difficult to account for tangible and intangible services in a combined model. The aim of this study is to extend the definition of ecosystem service trade-offs by using Bayesian Networks to capture the relationship between tangible and intangible ecosystem services. Tested is the potential of creating such a network based on existing literature and enhancement via expert elicitation. This study discusses the significance of expert elicitation to enhance the value of a Bayesian Network in data-restricted case studies, underlines the importance of inclusion of experts’ certainty, and demonstrates how multiple sources of knowledge can be combined into one model accounting for both tangible and intangible ecosystem services. Bayesian Networks appear to be a promising tool in this context, nevertheless, this approach is still in need of further refinement in structure and applicable guidelines for expert involvement and elicitation for a more unified methodology.

scholarly journals Using Bayesian Networks to identify musculoskeletal symptoms influencing the risk of developing Psoriatic Arthritis in people with psoriasis

Rheumatology ◽  
2021 ◽  
Author(s):  
Amelia Green ◽  
William Tillett ◽  
Neil McHugh ◽  
Theresa Smith ◽  
Keyword(s):  
Bayesian Networks ◽  
Expert Knowledge ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Musculoskeletal Symptoms ◽  
Musculoskeletal Symptom ◽  
The Uk ◽  
Primary Groups ◽  
Incident Cases

Abstract Objectives To explore Bayesian networks (BNs) in understanding the relationships between musculoskeletal symptoms and the development of PsA in people with psoriasis. Methods Incident cases of psoriasis were identified between 1998 and 2015 from the UK Clinical Research Practice Datalink. Musculoskeletal symptoms identified by medcodes were concatenated into primary groups, each made up of several sub-groups. Baseline demographics for gender, age, body mass index (BMI), psoriasis severity, alcohol use and smoking status were also extracted. Several BN structures were composed using a combination of expert knowledge and data-oriented modelling using: 1) primary musculoskeletal symptom groups, 2) musculoskeletal symptom sub-groups and 3) demographic variables. Predictive ability of the networks using the receiver operating characteristic curve (AUC) was calculated. Results Over one million musculoskeletal symptoms were extracted for the 90,189 incident cases of psoriasis identified, of which 1409 developed PsA. The BN analysis yielded direct relationships between gender, BMI, arthralgia, finger pain, fatigue, hand pain, hip pain, knee pain, swelling, back pain, myalgia and PsA. The best BN, achieved by using the more site-specific musculoskeletal symptom sub-groups, was 76% accurate in predicting the development of PsA in a test set and had an AUC of 0.73 (95% confidence interval (CI): 0.70-0.75). Conclusion The presented BN model may be a useful method to identify clusters of symptoms that predict the development of PsA with reasonable accuracy. Using a BN approach, we have shown that there are several symptoms which are predecessors of PsA, including fatigue, specific types of pain, and swelling.

scholarly journals Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 294 ◽  
Author(s):  
Xingping Sun ◽  
Chang Chen ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Expert Knowledge ◽  
Hybrid Optimization ◽  
Experimental Simulation ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.

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