{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T08:59:53Z","timestamp":1787907593761,"version":"build-2784847793"},"reference-count":32,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID\u201019 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID\u201019 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre\u2010process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID\u201019 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision\u2010maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID\u201019, CHD and hypertension, while 46.5% of people with COVID\u201019 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID\u201019 disease.<\/jats:p>","DOI":"10.1111\/exsy.12814","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T03:04:21Z","timestamp":1635217461000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["COVID\u201019 patient diagnosis and treatment data mining algorithm based on association rules"],"prefix":"10.1111","volume":"40","author":[{"given":"Zicheng","family":"Shan","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute Donghua University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-4664","authenticated-orcid":false,"given":"Wei","family":"Miao","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute Donghua University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"issue":"13","key":"e_1_2_7_2_1","first-page":"2973","article-title":"A proposed dynamic algorithm for association rules mining in big data","volume":"95","author":"Al\u2010Daher A. 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