Medical consumable usage control based on Canopy_K-means clustering and WARM
Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital....
Gespeichert in:
| Veröffentlicht in: | Journal of combinatorial optimization 2021-11, Vol.42 (4), p.722-739 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 739 |
|---|---|
| container_issue | 4 |
| container_start_page | 722 |
| container_title | Journal of combinatorial optimization |
| container_volume | 42 |
| creator | Yang, Ying Wu, Huijing Yan, Caixia |
| description | Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply. |
| doi_str_mv | 10.1007/s10878-019-00468-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2601564320</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2601564320</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-d8d5f37afe016ab42a971687bad084cd52b2a3215c00f2b921ce302ee23eb3023</originalsourceid><addsrcrecordid>eNp9kM1LxDAQxYMouK7-A54CnqOTpB_pcVn8wi6CKB5DmkyXXbppTdrD_ve2VvDmaX4M771hHiHXHG45QH4XOahcMeAFA0iykU7Igqe5ZEKp7HRkqQTLCkjPyUWMewAYOVmQcoNuZ01DbevjcDBVg3SIZovTog9tQysT0dHW07XxbXfUL-yAxkdqmyH2GHZ-S4139HP1trkkZ7VpIl79ziX5eLh_Xz-x8vXxeb0qmZW86JlTLq1lbmoEnpkqEabIeabyyjhQiXWpqISRgqcWoBZVIbhFCQJRSKxGkEtyM-d2of0aMPZ63w7Bjye1yICnWSIFjCoxq2xoYwxY6y7sDiYcNQc9tabn1vTYmv5pTU8mOZtiN72G4S_6H9c3gCBu6w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2601564320</pqid></control><display><type>article</type><title>Medical consumable usage control based on Canopy_K-means clustering and WARM</title><source>SpringerLink Journals - AutoHoldings</source><creator>Yang, Ying ; Wu, Huijing ; Yan, Caixia</creator><creatorcontrib>Yang, Ying ; Wu, Huijing ; Yan, Caixia</creatorcontrib><description>Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.</description><identifier>ISSN: 1382-6905</identifier><identifier>EISSN: 1573-2886</identifier><identifier>DOI: 10.1007/s10878-019-00468-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Canopies ; Cluster analysis ; Clustering ; Combinatorics ; Control methods ; Convex and Discrete Geometry ; Mathematical Modeling and Industrial Mathematics ; Mathematics ; Mathematics and Statistics ; Operations Research/Decision Theory ; Optimization ; Theory of Computation ; Vector quantization</subject><ispartof>Journal of combinatorial optimization, 2021-11, Vol.42 (4), p.722-739</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-d8d5f37afe016ab42a971687bad084cd52b2a3215c00f2b921ce302ee23eb3023</citedby><cites>FETCH-LOGICAL-c319t-d8d5f37afe016ab42a971687bad084cd52b2a3215c00f2b921ce302ee23eb3023</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10878-019-00468-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10878-019-00468-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Yang, Ying</creatorcontrib><creatorcontrib>Wu, Huijing</creatorcontrib><creatorcontrib>Yan, Caixia</creatorcontrib><title>Medical consumable usage control based on Canopy_K-means clustering and WARM</title><title>Journal of combinatorial optimization</title><addtitle>J Comb Optim</addtitle><description>Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.</description><subject>Algorithms</subject><subject>Canopies</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Combinatorics</subject><subject>Control methods</subject><subject>Convex and Discrete Geometry</subject><subject>Mathematical Modeling and Industrial Mathematics</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Operations Research/Decision Theory</subject><subject>Optimization</subject><subject>Theory of Computation</subject><subject>Vector quantization</subject><issn>1382-6905</issn><issn>1573-2886</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kM1LxDAQxYMouK7-A54CnqOTpB_pcVn8wi6CKB5DmkyXXbppTdrD_ve2VvDmaX4M771hHiHXHG45QH4XOahcMeAFA0iykU7Igqe5ZEKp7HRkqQTLCkjPyUWMewAYOVmQcoNuZ01DbevjcDBVg3SIZovTog9tQysT0dHW07XxbXfUL-yAxkdqmyH2GHZ-S4139HP1trkkZ7VpIl79ziX5eLh_Xz-x8vXxeb0qmZW86JlTLq1lbmoEnpkqEabIeabyyjhQiXWpqISRgqcWoBZVIbhFCQJRSKxGkEtyM-d2of0aMPZ63w7Bjye1yICnWSIFjCoxq2xoYwxY6y7sDiYcNQc9tabn1vTYmv5pTU8mOZtiN72G4S_6H9c3gCBu6w</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Yang, Ying</creator><creator>Wu, Huijing</creator><creator>Yan, Caixia</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20211101</creationdate><title>Medical consumable usage control based on Canopy_K-means clustering and WARM</title><author>Yang, Ying ; Wu, Huijing ; Yan, Caixia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-d8d5f37afe016ab42a971687bad084cd52b2a3215c00f2b921ce302ee23eb3023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Canopies</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Combinatorics</topic><topic>Control methods</topic><topic>Convex and Discrete Geometry</topic><topic>Mathematical Modeling and Industrial Mathematics</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Operations Research/Decision Theory</topic><topic>Optimization</topic><topic>Theory of Computation</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Ying</creatorcontrib><creatorcontrib>Wu, Huijing</creatorcontrib><creatorcontrib>Yan, Caixia</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of combinatorial optimization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Ying</au><au>Wu, Huijing</au><au>Yan, Caixia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Medical consumable usage control based on Canopy_K-means clustering and WARM</atitle><jtitle>Journal of combinatorial optimization</jtitle><stitle>J Comb Optim</stitle><date>2021-11-01</date><risdate>2021</risdate><volume>42</volume><issue>4</issue><spage>722</spage><epage>739</epage><pages>722-739</pages><issn>1382-6905</issn><eissn>1573-2886</eissn><abstract>Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10878-019-00468-0</doi><tpages>18</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1382-6905 |
| ispartof | Journal of combinatorial optimization, 2021-11, Vol.42 (4), p.722-739 |
| issn | 1382-6905 1573-2886 |
| language | eng |
| recordid | cdi_proquest_journals_2601564320 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Canopies Cluster analysis Clustering Combinatorics Control methods Convex and Discrete Geometry Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Theory of Computation Vector quantization |
| title | Medical consumable usage control based on Canopy_K-means clustering and WARM |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T19%3A38%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Medical%20consumable%20usage%20control%20based%20on%20Canopy_K-means%20clustering%20and%20WARM&rft.jtitle=Journal%20of%20combinatorial%20optimization&rft.au=Yang,%20Ying&rft.date=2021-11-01&rft.volume=42&rft.issue=4&rft.spage=722&rft.epage=739&rft.pages=722-739&rft.issn=1382-6905&rft.eissn=1573-2886&rft_id=info:doi/10.1007/s10878-019-00468-0&rft_dat=%3Cproquest_cross%3E2601564320%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2601564320&rft_id=info:pmid/&rfr_iscdi=true |