Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
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We have developed a web application for the collection of EPR's (Electronic PatientRecords) from uterine adnexal masses pre-operatively examined with transvaginal ultra-sonography. The application has been used intensively since November 2000 by 9 of the 19 international centers that joined the International Ovarian Tumor Analysis (IOTA) consortium. At the moment of writing, the IOTA database contained 68 parameters for 1150masses. Here we report the design and implementation of the generic web-based clinicaldata entry system and describe the advantages and drawbacks that we have experiencedwhile developing, using, and maintaining the system. The data model, the user interface,the help system, the constraints (mandatory/optional), and the quality checking were allbased on the medical protocol created by the IOTA consortium. The data collection systemhas become an open and transparent implementation of the formalized protocol. It coversthe complete path of the patient data from the clinical situation to the finalized database.This approach provides new types of possibilities for the data analysis since all aspects ofthe data collection are documented and formally available to the data analyst. The IOTAwebsite can be found at https://www.iota-group.org which also serves as the entry point forthe secure EPR application.