Volume 12, Issue 1 (5-2018)
payavard 2018, 12(1): 44-52 |
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Safdari R, Shahmoradi L, Daneshvar M, Pourtorkan E, Gholamzadeh M. Decision Modeling for Diagnosis and Screening of Ovarian Epithelial Cancer. payavard 2018; 12 (1) :44-52
URL: http://payavard.tums.ac.ir/article-1-6476-en.html
URL: http://payavard.tums.ac.ir/article-1-6476-en.html
1- Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- Associate Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- Fellowship of gyncology, Internal Medicine Department, Imam Khomeini Hospital, Karaj, Iran
4- Master of Science in Medical Informatics, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,elmirapt@yahoo.com
5- Master of Sciences Student in Health Information Technology, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- Associate Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- Fellowship of gyncology, Internal Medicine Department, Imam Khomeini Hospital, Karaj, Iran
4- Master of Science in Medical Informatics, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,
5- Master of Sciences Student in Health Information Technology, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (4156 Views)
Background and Aim: The Ovarian epithelial cancer is one of the most deadly types of cancers in women.Thus, the purpose of this study was to investigate the most effective factors in predicting and detecting Ovarian cancer in the form of a decision tree to facilitate the Ovarian cancer diagnosis.
Materials and Methods: The present study was a descriptive-developmental study. The main research tool applied in this study was a checklist which was designed based on the medical records, published studies, scientific references, and expert consultation.To determine the content validity of the checklist, the CVR method was applied. Next, survey research was done with aid of Likert-based checklist based on expert opinions in gynecology. Finally, to develop the decision tree, the results of the expert survey were analyzed and the final model was implemented based on the survey results.
Results: The data elements of final decision tree were derived from the result of expert surveys, guidelines, clinical pathways and strategies in context of diagnosis and screening of Ovarian cancer. The leaf nodes in the tree include different types of tumor markers, following up, therapeutic measures, and referrals. The accuracy of the decision tree was approved by the experts. The most important tumor markers that obtained from the decision model in this study were CA19-9, ROMA (CA125 + HE4) and CEA.
Conclusion: Clinical decision models can provide specific diagnosis and therapeutic suggestions by creating patient information integration framework. The model developed in this study can improve the diagnosis of epithelial Ovarian cancer considerably by facilitating decision making.
Materials and Methods: The present study was a descriptive-developmental study. The main research tool applied in this study was a checklist which was designed based on the medical records, published studies, scientific references, and expert consultation.To determine the content validity of the checklist, the CVR method was applied. Next, survey research was done with aid of Likert-based checklist based on expert opinions in gynecology. Finally, to develop the decision tree, the results of the expert survey were analyzed and the final model was implemented based on the survey results.
Results: The data elements of final decision tree were derived from the result of expert surveys, guidelines, clinical pathways and strategies in context of diagnosis and screening of Ovarian cancer. The leaf nodes in the tree include different types of tumor markers, following up, therapeutic measures, and referrals. The accuracy of the decision tree was approved by the experts. The most important tumor markers that obtained from the decision model in this study were CA19-9, ROMA (CA125 + HE4) and CEA.
Conclusion: Clinical decision models can provide specific diagnosis and therapeutic suggestions by creating patient information integration framework. The model developed in this study can improve the diagnosis of epithelial Ovarian cancer considerably by facilitating decision making.
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