Maethaphan Kitporntheranunt1, Watcharachai Wiriyasuttiwong. 1. Department of Obstetrics and Gynecology, HRH Maha Chakri Sirindhorn Medical Center, Faculty of Medicine, Srinakharinwirot University, Nakhon Nayok, Thailand. sumate@swu.ac.th
Abstract
OBJECTIVE: To developed a software-based medical expert system for supporting in the diagnosis of ectopic pregnancy. MATERIAL AND METHOD: This system was developed to facilitate the knowledge creation and inference engine. The diagnostic process used the interactive backward chaining inference algorithm. The medical knowledge base was represented as production rules which performed in tree structure. The system designed to interact with users in question information form. After that, the clinical data from medical records of patients were applied to the system by physician retrospectively. RESULTS: Thirty-two medical records of women who diagnosed ectopic pregnancy were reviewed. The mean patients' age was 27.7 years. The majority of the patients were multigravida gestation. There were twenty-one patients who did not have any risk factors of ectopic pregnancy. 31 of patients' clinical data applied to the system successfully. All of them had correspondent diagnosis with established ectopic pregnancy. CONCLUSION: This medical expert system was found to be a good tool to facilitate the decision making in the diagnosis of ectopic pregnancy.
OBJECTIVE: To developed a software-based medical expert system for supporting in the diagnosis of ectopic pregnancy. MATERIAL AND METHOD: This system was developed to facilitate the knowledge creation and inference engine. The diagnostic process used the interactive backward chaining inference algorithm. The medical knowledge base was represented as production rules which performed in tree structure. The system designed to interact with users in question information form. After that, the clinical data from medical records of patients were applied to the system by physician retrospectively. RESULTS: Thirty-two medical records of women who diagnosed ectopic pregnancy were reviewed. The mean patients' age was 27.7 years. The majority of the patients were multigravida gestation. There were twenty-one patients who did not have any risk factors of ectopic pregnancy. 31 of patients' clinical data applied to the system successfully. All of them had correspondent diagnosis with established ectopic pregnancy. CONCLUSION: This medical expert system was found to be a good tool to facilitate the decision making in the diagnosis of ectopic pregnancy.