farzaneh feizmanesh

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.

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.

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.

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.

With the extensive use of electronic medical records systems, a large amount of medical text data is produced daily in the hospitals and other medical environments that organizing this text information is important and necessary. Also, a need to automatically retrieve useful knowledge from this data to help clinicians is felt. In order to extract the hidden values in the medical text documents, text mining can be used in the field of health.

In this review study, SID, Magiran, Pubmed, ScienceDirect, IEEE, and Google Scholar databases were searched with the keywords including "Text Mining” AND "Medicine”، "Clinical Text Mining” AND "Predict”،, "knowledge discovery in medical text” and "Text Mining for Medical and Healthcare” in the English databases and keywords such as "text mining” AND " Discovering Knowledge in Medicine” in the Persian databases. Then, all articles that somehow refer to Medical knowledge discovery and text mining applications in the field of health were selected.

Text mining is one of the important and powerful techniques for extracting information from health information systems. Text mining in clinical data provides potential for new discoveries and it also improves efficiency and communication in hospital systems for doctors and hospital administrators.

Nowadays, text mining in clinical documentation, is one of the developed technologies for the discovery of medical knowledge that its use in medical databases is essential to achieving immediate access to important health resources, and its application can improve patient care and reduce medical costs