Determination of risk factors for predicting pulmonary embolism using Bayesian networks

How to Cite:Farzaneh FeizmaneshAliasghar SaghaeiKeivan Gohari MoghadamDetermination of risk factors for predicting pulmonary embolism using Bayesian networks.koomesh.20(4):e153008.

Abstract

Introduction: Pulmonary embolism is the third leading cause of cardiovascular death after Myocardial infarction and stroke. At the same time, it is the most preventable cause of death for hospitalized patients. Importantly the diagnosis and prediction of pulmonary embolism requires flexible decision-making models, both for the presence of clinical interventions as well as for the variety of local diagnostic resources, Bayesian networks that fully meet these needs. Accordingly determining the risk factors for pulmonary embolism in hospitalized patients and presenting the model for predicting its occurrence through modeling using Bayesian networks have been proposed as a therapeutic necessity. Materials and Methods: The present research is descriptive-analytic study. The data used in the study included risk factors affecting the pulmonary embolism and the history of hospitalized patients in pulmonary section of Shariati hospital in Tehran were collected in Excel format. Bayesian prediction model in two modes (risk factors determined using the proposed scenario and risk factors according to the expert physician) is obtained using GENIE software and the accuracy of the diagnosis of pulmonary embolism was evaluated. Results: The results showed that among the risk factors of the disease, the history of thromboembolic pulmonary, history of deep vein thrombosis, body mass index above 30, recent surgery, immobilization of long-term, SLE, antiphospholipid syndrome, heart failure and pneumonia respectively, are the most important risk factors for pulmonary embolism. And the model predicts the scenario proposed has better performance. Conclusion: Such plans can facilitate the process of assessing the risk of pulmonary embolism in hospitalized patients, in order to facilitate appropriate preventive measures, and to improve preventive methods and, consequently, diagnosis and treatment programs.

Keywords

References

1.
Bellazzi R, Zupan B. Predictive data mining in clinical medicine: Current issues and guidelines. Int J Med Inform 2008; 77: 81-97.
2.
McPhee SJ, Papadakis MA, Rabow MW EM-H. Current medical diagnosis & treatment 2010. McGraw-Hill Medical, 2010.
3.
Bates DW. Using information technology to reduce rates of medication errors in hospitals. Br Med J 2000; 320: 788-791.
4.
Bali RK. Clinical Knowledge Management. Coventry University, UK: Coventry University, UK, 2005.
5.
Korb, Kevin B. and AEN. Bayesian artificial intelligence. Florida: Chapman & Hall/CRC, 2003.
6.
Goldhaber SZ, Bounameaux H. Pulmonary embolism and deep vein thrombosis. The Lancet 2012; 379: 1835-1846.
7.
Yoo HH, Queluz TH, El Dib R. Anticoagulant treatment for subsegmental pulmonary embolism. Cochrane Database of Systematic Reviews. 2014 (4).
8.
Cao J, Cao Y, Chen H, Gao W, Wang Y. Systematic review and meta-analysis for thrombolysis treatment in patients with acute submassive pulmonary embolism. Patient Prefer Adherence 2014; 8: 275-282.
9.
Agharezaei L, Agharezaei Z, Nemati A, Bahaadinbeigy K, Keynia F, Baneshi MR, et al. The prediction of the risk level of pulmonary embolism and deep vein thrombosis through artificial neural network. Acta Inform Med 2016; 24: 354-359.
10.
Fedullo PF, Tapson VF. Clinical practice. The evaluation of suspected pulmonary embolism. N Engl J Med 2003; 349: 1247-1256.
11.
Pardanani A, Lasho TL, Hussein K, Schwager SM, Finke CM, Pruthi RK, et al. JAK2V617F mutation screening as part of the hypercoagulable work-up in the absence of splanchnic venous thrombosis or overt myeloproliferative neoplasm: assessment of value in a series of 664 consecutive patients. Mayo Clin Proc 2008; 83: 457-459.
12.
Sadoughi Farahnaz SA. Applications of artificial intelligence in clinical decision making: opportunities and challenges. Heal Inf Manag 2011; 5: 440-445.
13.
Agha Rezaei G, Tofighi Sh, Nemati A, Agha Rezaei L BK. Clinical decision support systems to reduce the risk of venous thrombosis and pulmonary embolism. Hosp Q 2012; 2. (Persian).
14.
Serpen G, Tekkedil D, Orra M. A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis. Comput Biol Med 2008; 38: 204-220.
15.
Tang L, Wang L, Pan S, Su Y, Chen Y. A neural network to pulmonary embolism aided diagnosis with a feature selection approach. InBiomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on 2010; pp: 2255-2260. IEEE.
16.
Berkol G, Kula S, Yldz O. Machine learning techniques in diagnosis of pulmonary embolism pulmoner emboli tansnda makine orenmesi teknikleri. J Clin Anal Med 2015; 6: 729-732.
17.
Jafari M. The design of the medical decision-making system helps the diagnosis of pulmonary embolism using artificial neural networks, Master of Science (MSc), Tehran, Medical Sciences of Tehran, 2015. (Persian).
18.
Serpen G, Tekkedil D, Orra M. A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis. Comput Biol Med 2008; 38: 204-220.
19.
Tang L, Wang L, Pan S, Su Y, Chen Y. A neural network to pulmonary embolism aided diagnosis with a feature selection approach. InBiomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on 2010; pp: 2255-2260 IEEE.
20.
Tourassi G, Floyd C, Sostman H, Coleman R. Artificial neural network for diagnosis of acute pulmonary embolism: effect of case and observer selection. Radiology 1995; 194: 889-893.
21.
Kline J, Novobilski A, Kabrhel C, Richman P. Derivation and validation of a Bayesian network to predict pretest probability of venous thromboembolism. Ann Emerg Med 2005; 45: 282-290.
22.
Luciani D, Cavuto S, Antiga L, Miniati M, Monti S, Pistolesi M, et al. Bayes pulmonary embolism assisted diagnosis: a new expert system for clinical use. Emerg Med J 2007; 24: 157-164.

comments