Building the pelvic endometriosis knowledge base software

Maethaphan Kitporntheranunt, Watcharachai Wiriyasuttiwong, Anuchit Nirapai, Athit Boonmeepipit
In: วารสารการแพทย์และวิทยาศาสตร์สุขภาพ (Journal of Medicine and Health Science); Vol 19, No 2 (2555): ปีที่ 19 ฉบับที่ 2 สิงหาคม 2555; 29-37 · 2012 · vol. 19(2) , pp. 29–37 · W1597786044
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A medical expert system using backward chaining and production rules accurately diagnosed pelvic endometriosis in 85.7% of reviewed cases, demonstrating its utility for clinical decision-making support.

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This study aimed to develop a software-based medical expert system to support the diagnosis of pelvic endometriosis using an interactive backward chaining inference algorithm. The researchers retrospectively reviewed 35 medical records from women with confirmed pelvic endometriosis, extracting clinical data on symptoms such as dysmenorrhea, chronic pelvic pain, and infertility. The system successfully recorded and diagnosed 85.7% of the cases, with its diagnostic outputs corresponding accurately to established medical records. This paper is centrally about endometriosis — specifically focusing on the development of a decision-support tool for diagnosing pelvic endometriosis.

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Abstract

The objective of this study is to develop a software-based medical expert system supporting the diagnosis of pelvic endometriosis. This system was developed to facilitate the creation of knowledge and inference engine. The diagnostic process used the interactive backward chaining inference algorithm. The medical knowledge data base was represented as production rules which represented in tree structures. The system was designed to interact with users in question information format. The clinical data from medical records of Gynecological out-patient clinic at HRH Maha Chakri Sirindhorn Medical Center were applied to the system by physician retrospectively. In this study, 35 medical records of women diagnosed with pelvic endometriosis were reviewed. The three most common presenting symptoms were dysmenorrhea, chronic pelvic pain and infertility, respectively. All of the patients were investigated with transvaginal sonography. Twenty-one patients had no histological studies. The clinical data of 30 patients accounted for 85.7 % were recorded successfully to the medical expert system. The diagnosis of these patients from the system corresponded with the previous data from the medical records of established pelvic endometriosis. Taken together, these data suggest that this medical expert system is a good tool to facilitate the decision making process in the diagnosis of pelvic endometriosis.
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Building the pelvic endometriosis knowledge base software Keywords: Medical expert system, Pelvic endometriosis, Medical knowledge-based systemAbstract The objective of this study is to develop a software-based medical expert system supporting the diagnosis of pelvic endometriosis. This system was developed to facilitate the creation of knowledge and inference engine. The diagnostic process used the interactive backward chaining inference algorithm. The medical knowledge data base was represented as production rules which represented in tree structures. The system was designed to interact with users in question information format. The clinical data from medical records of Gynecological out-patient clinic at HRH Maha Chakri Sirindhorn Medical Center were applied to the system by physician retrospectively. In this study, 35 medical records of women diagnosed with pelvic endometriosis were reviewed. The three most common presenting symptoms were dysmenorrhea, chronic pelvic pain and infertility, respectively. All of the patients were investigated with transvaginal sonography. Twenty-one patients had no histological studies. The clinical data of 30 patients accounted for 85.7 % were recorded successfully to the medical expert system. The diagnosis of these patients from the system corresponded with the previous data from the medical records of established pelvic endometriosis. Taken together, these data suggest that this medical expert system is a good tool to facilitate the decision making process in the diagnosis of pelvic endometriosis.Downloads Published 2012-11-02 Issue Section Original Article

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endometriosischronic_pelvic_paindysmenorrheainfertility

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