Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records.
Objective. Short-term or long-term connections between different diseases have not been fully acknowledged. This study was aimed at exploring the network association pattern between disorders that occurred in the same individual by using the association rule mining technique. Methods. Raw data were...
| Published in: | BioMed Research International pp. 1 - 17 |
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| Main Authors: | , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
5/13/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156864722&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156864722 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 5/13/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 156864722 156864722 156864722 10.1155/2022/2199317 156864722 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records. aug: au: Ma, He Ding, Jingjing Liu, Mei Liu, Ying affil: Department of Medical Records & Statistics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221000, China sug: subj: Electronic Health Records Algorithms Data Mining Medical Records Diagnosis Human Descriptive Statistics Data Analysis Software International Classification of Diseases Male Female Middle Age Aged Aged, 80 and Over Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Objective. Short-term or long-term connections between different diseases have not been fully acknowledged. This study was aimed at exploring the network association pattern between disorders that occurred in the same individual by using the association rule mining technique. Methods. Raw data were extracted from the large-scale electronic medical record database of the affiliated hospital of Xuzhou Medical University. 1551732 pieces of diagnosis information from 144207 patients were collected from 2015 to 2020. Clinic diagnoses were categorized according to "International Classification of Diseases, 10th revision". The Apriori algorithm was used to explore the association patterns among those diagnoses. Results. 12889 rules were generated after running the algorithm at first. After threshold filtering and manual examination, 110 disease combinations (support ≥ 0.001 , confidence ≥ 60 % , lift > 1) with strong association strength were obtained eventually. Association rules about the circulatory system and metabolic diseases accounted for a significant part of the results. Conclusion. This research elucidated the network associations between disorders from different body systems in the same individual and demonstrated the usefulness of the Apriori algorithm in comorbidity or multimorbidity studies. The mined combinations will be helpful in improving prevention strategies, early identification of high-risk populations, and reducing mortality. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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