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Development of an Expert Model to Assess Falls from Height Hazards in Construction Sites

Carlo Argiolas, Alessandro Carbonari, Emanuela Quaquero
2014 Journal of Civil Engineering and Architecture  
To this aim, the features offered by Bayesian networks have been exploited.  ...  As any programmatic actions cannot deal with the unpredictable nature of many risk dynamics, an attempt to improve the current approach for safety management in the construction industry will be presented  ...  risks resulting from factors adverse to health 1 Manager of a building company All the aspects related to production can affect their contributions 1 Occupational safety and health inspector  ... 
doi:10.17265/1934-7359/2014.05.001 fatcat:5t5xmrbk5nfsjadtbjlgk7dwzi

Applying Nonparametric Methods to Analyses of Short-Term Fine Particulate Matter Exposure and Hospital Admissions for Cardiovascular Diseases among Older Adults

Louis Cox, Xiaobin Liu, Liuhua Shi, Ke Zu, Julie Goodman
2017 International Journal of Environmental Research and Public Health  
We next used a Bayesian network learning algorithm to identify conditional dependencies between CVD HAs of older men and women and several predictor variables.  ...  Short-term exposure to fine particulate matter (PM 2.5 ) has been associated with increased risks of cardiovascular diseases (CVDs), but whether such associations are supportive of a causal relationship  ...  The authors had the sole responsibility for  ... 
doi:10.3390/ijerph14091051 pmid:28895893 pmcid:PMC5615588 fatcat:s7zemgbt3ndj7flddedfeyohce

A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions [article]

Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, Susan P Fisher-Hoch, Joseph B Mccormick, Julian Carvajal Rico
2022 arXiv   pre-print
This paper proposes a model predictive control functional continuous time Bayesian network, an online recursive method to examine the impact of various lifestyle behavioral changes on the emergence trajectories  ...  (diet, exercise habits, tobacco use, alcohol use) and four non-modifiable risk factors, including socio-demographic information (age, gender, education, marital status).  ...  UL1 TR000371 from the National Center for Advancing Translational Sciences. This study was also partially funded by National Institutes of Health (NIH/NIGMS) Award grant no. 1SC2GM118266-01.  ... 
arXiv:2205.13639v1 fatcat:px6hv3e2tfapli6qlpxt2lqvhi

Analysis of risk factors of hip fracture with causal Bayesian networks

Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott, Michel Ducher
2014 International Work-Conference on Bioinformatics and Biomedical Engineering  
We explore a practical approach to learn a plausible causal Bayesian network from a combination of non-experimental data and qualitative assumptions that are deemed likely by health experts.  ...  The method is based on the incorporation of prior expert knowledge in the form of partial pairwise ordering constraints between variables into a recent constraint-based Bayesian network structure learning  ...  Acknowledgments The authors thank Marco Scutari for sharing his bnlearn package in R.  ... 
dblp:conf/iwbbio/AussemCKGSD14 fatcat:wweyc57fjnh77mk3f5qt6h7qnm

Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm

Hongye Zhong, Jitian Xiao
2017 Scientific Programming  
Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference.  ...  Fusion node is an information fusion model for constructing prediction systems.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2017/1901876 fatcat:b3yrbejvbzh3bi7etsoeykzivu

The UKB envirome of depression: from interactions to synergistic effects

Gabor Hullam, Peter Antal, Peter Petschner, Xenia Gonda, Gyorgy Bagdy, Bill Deakin, Gabriella Juhasz
2019 Scientific Reports  
The primary aim of this work was to create a Bayesian dependency map of environmental factors of depression, including life stress, social and lifestyle factors, using the UK Biobank data to determine  ...  As a complementary approach, we also investigated the non-linear, synergistic multi-factorial risk of the UKB envirome on depression using deep neural network architectures.  ...  design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.  ... 
doi:10.1038/s41598-019-46001-5 pmid:31278308 pmcid:PMC6611783 fatcat:xqnjq6bspvhf5cltirbgleptq4

Situation awareness in crowdsensing for disease surveillance in crisis situations

Peter Haddawy, Lutz Frommberger, Tomi Kauppinen, Giorgio De Felice, Prae Charkratpahu, Sirawaratt Saengpao, Phanumas Kanchanakitsakul
2015 Proceedings of the Seventh International Conference on Information and Communication Technologies and Development - ICTD '15  
for crisis management remains a largely unexplored area of research.  ...  The communication workflow in mobile4D-SA supports interaction between crowdsensed information, system predictions, and multifaceted communication between authorities and affected people on the ground.  ...  We thank the Ministry of Agriculture and Forestry of Lao PDR for their longstanding collaboration and the mobile4D student project participants for their contributions.  ... 
doi:10.1145/2737856.2737879 dblp:conf/ictd/HaddawyFKFCSK15 fatcat:rfffnx475nevhcjlqhhzwgzata

Leveraging a Bayesian Network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria

Jeanne-Marie Lawrence, Niamat Ullah Ibne Hossain, Raed Jaradat, Michael Hamilton
2020 International Journal of Disaster Risk Reduction  
The United States government has identified the health care sector as part of the critical infrastructure for homeland security to protect citizens against health risks arising from terrorism, natural  ...  A causality Bayesian model is developed to depict linkages between risk events and quantify the associated cumulative risk.  ...  There are several advantages of using a Bayesian network model in risk analysis.  ... 
doi:10.1016/j.ijdrr.2020.101607 pmid:32346504 pmcid:PMC7187851 fatcat:ytdxxspztvfpjgbkkvp4a732xi

A Survey of Different Approaches of Machine Learning in Healthcare Management System

Dr. Krishan Kumar Goyal, Aejaz Hassan Paray
2019 International journal of advanced networking and applications  
For analysis of medical data, medical experts use the machine learning tools and techniques to identify the risks and to provide proper diagnosis and treatment.  ...  Machine Learning is used in most of the fields and Especially in health care sector it takes much more benefits through proper decision and prediction techniques.  ...  Machine learning and Artificial Intelligence changing healthcare by using predictive analytics for proper treatment and decision without any risk factor.  ... 
doi:10.35444/ijana.2019.11032 fatcat:7g7dvqhk4fdxdhwlxo43qhuwbu

Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston

Margaret Reid, Julia Gunn, Snehal Shah, Michael Donovan, Rosalind Eggo, Steven Babin, Ivanka Stajner, Eric Rogers, Katherine B. Ensor, Loren Raun, Jonathan I. Levy, Ian Painter (+6 others)
2016 Online Journal of Public Health Informatics  
of known risk factors for which evidence is routinely available.  ...  Attendees valued the direct exchange of information among public health practitioners, system designers, and modelers.  ...  Acknowledgements We thank the Boston Public Health Commission and particularly Executive Director Monica Valdes Lupi JD, MPH for hosting and leading the consultancy.  ... 
doi:10.5210/ojphi.v8i3.6902 pmid:28210420 pmcid:PMC5302473 fatcat:3lr6cldjifddllkz676sskloyi

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting

David M. Vock, Julian Wolfson, Sunayan Bandyopadhyay, Gediminas Adomavicius, Paul E. Johnson, Gabriela Vazquez-Benitez, Patrick J. O'Connor
2016 Journal of Biomedical Informatics  
We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees  ...  Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5 years) based on individual patient characteristics  ...  Acknowledgments Funding sources This work was partially supported by NHLBI grant R01HL102144-01, NIH grant UL1TR000114, and AHRQ grant R21HS017622-01.  ... 
doi:10.1016/j.jbi.2016.03.009 pmid:26992568 pmcid:PMC4893987 fatcat:ikux2my3drhpdbectlx53xtymq

Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank [article]

Tiago Azevedo, Richard A.I. Bethlehem, David J. Whiteside, Nol Swaddiwudhipong, James B. Rowe, Pietro Lio, Timothy Rittman
2022 medRxiv   pre-print
We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking.  ...  To address this challenge we train a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD-score representing the probability of AD using structural MRI data in the  ...  We report the results of a number of risk factors in figure 6 and other health markers in figure 7 .  ... 
doi:10.1101/2022.01.05.22268795 fatcat:mdjrto6stjfx3ntnoqphuav6nu

Analysis Factors That Influence Escalator-Related Injuries in Metro Stations Based on Bayesian Networks: A Case Study in China

Yingying Xing, Shengdi Chen, Shengxue Zhu, Jian Lu
2020 International Journal of Environmental Research and Public Health  
Then, 950 escalator-related injuries were used to estimate the posterior probabilities of the Bayesian network with expectation–maximization (EM) algorithm.  ...  risk factors that affect the escalator safety in metro stations.  ...  The authors thank the editor and reviewer for their valuable suggestions. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijerph17020481 pmid:31940854 pmcid:PMC7014387 fatcat:bsw4abrelzatrbl32wjmlirsne

Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

Sookyung Hyun, Susan Moffatt-Bruce, Cheryl Cooper, Brenda Hixon, Pacharmon Kaewprag
2019 JMIR Medical Informatics  
The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the  ...  Using an extremely large, electronic health record-derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled  ...  Acknowledgments The authors would like to thank Tara Payne, Marcia Belcher, and Information Warehouse staff for their assistance with data extraction.  ... 
doi:10.2196/13785 pmid:31322127 pmcid:PMC6670273 fatcat:aqukpnrjfnfixmpb3hjfi36mz4

Development of a Bayesian model to estimate health care outcomes in the severely wounded

Alexander Stojadinovic, Felix
2010 Journal of Multidisciplinary Healthcare  
Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic  ...  These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations.  ...  Acknowledgments We have a government-sponsored translational research program, which has partnered with DecisionQ Corporation to develop predictive models to advance personalized medicine within the Department  ... 
doi:10.2147/jmdh.s11537 pmid:21197361 pmcid:PMC3004592 fatcat:sz453sznkbcmlcds5ljaamikbi
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