{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T13:58:44Z","timestamp":1770040724160,"version":"3.49.0"},"reference-count":31,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Systems Biology"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We analyzed the symptoms composition of Interstitial Cystitis (IC), the regularity of the evolution of symptoms before and after treatment, and the visualization of the community network, to provide a reference for clinical diagnosis and treatment of Interstitial Cystitis. Based on the outpatient electronic case data of 552 patients with Interstitial Cystitis, we used a complex network community discovery algorithm, directed weighted complex network, and Sankey map to mine the data of the symptoms composition of Interstitial Cystitis, the evolution of symptoms before and after treatment and the visualization of the community network, to analyze the epidemiological characteristics of interstitial cystitis symptoms in the real world. By the community division of the complex network of interstitial cystitis symptoms, We finally obtained three core symptom communities. Among them, symptom community A (bladder\u2010related symptoms) is the symptom community with the highest proportion of nodes (60.00%) in the complex network of Interstitial Cystitis, symptom community B (non\u2010bladder\u2010related symptoms 1) ranks second (32.00%) in a complex network of Interstitial Cystitis, and symptom community C (non\u2010bladder\u2010related symptoms 2) has the lowest proportion (8.00%). There is a complex evolutionary relationship between the symptoms of Interstitial Cystitis before and after treatment. Among the single symptoms before and after treatment, the decreased rate of Day frequency is 93.22%, and the reduced urgency rate is 93.07%. The decline rate of Nocturia was 82.33%. From the perspective of different communities, the\u00a0overall symptoms of symptom community A decreased by 34.39% after treatment, the general symptoms of symptom community B decreased by 35.37%, and the prevalent symptoms of symptom community C decreased by 71.43%. In the case of using diet regulation treatment to treat bladder pain, the cure rate of bladder pain can reach 22.67%. The cure rate of burning in bladders can get 15.38% with Percutaneous Sacral neuromodulation, 96.95% with diet regulation treatment, and 100% with Percutaneous Sacral neuromodulation. When using behavioral physiotherapy to treat bladder pain, 3.57% of the patient's symptoms change to bladder discomfort; 4% of the patient's symptoms change to bladder discomfort when using oral medicine to treat bladder pain.Symptom research methods based on community findings can effectively explore the rule of symptom outcome of Interstitial Cystitis before and after treatment, and the results are highly interpretable by professionals.<\/jats:p>\n                  <jats:p>\n                    The cover image is based on the Original Article\n                    <jats:italic>Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm<\/jats:italic>\n                    by Lei Pang et\u00a0al.,\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1049\/syb2.12083\">https:\/\/doi.org\/10.1049\/syb2.12083<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1049\/syb2.12083","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T23:09:25Z","timestamp":1699916965000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm"],"prefix":"10.1049","volume":"18","author":[{"given":"Lei","family":"Pang","sequence":"first","affiliation":[{"name":"Urology Department of Shanxi Provincial People's Hospital  Taiyuan Shanxi Province China"},{"name":"The First Clinical Medical College of Shanxi Medical University  Taiyuan Shanxi Province 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