A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory
Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome th...
Gespeichert in:
| Veröffentlicht in: | Expert systems 2019-06, Vol.36 (3), p.n/a |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | n/a |
|---|---|
| container_issue | 3 |
| container_start_page | |
| container_title | Expert systems |
| container_volume | 36 |
| creator | Yang, Yan Hu, Junhua Liu, Yongmei Chen, Xiaohong |
| description | Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment. |
| doi_str_mv | 10.1111/exsy.12377 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2233735941</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2233735941</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3497-a3263c51389343293f2a21777124d94a4f6246c02ac04659f54cf6414942b3353</originalsourceid><addsrcrecordid>eNp9kLtOwzAUhi0EEqWw8ASW2JBSfIvdjFVVLlIlBkCCKXJth7pK4mA7KtlY2HlGnoSUVGLjLGf5_vPrfACcYzTB_VyZ99BNMKFCHIARZnyaIJqxQzBChPOECYKOwUkIG4QQFoKPwOcMVm0ZbWO8dRquu5W3GmqjbLCuhqFtGucjrJw2JSych5XRVskSaitfaxdsgLLWMHojY2XqCFcyGA13UVvZUnobrdkza2_M98fXVnZ_BXFtnO9OwVEhy2DO9nsMnq4Xj_PbZHl_czefLRNFWSYSSQmnKsV0mlFGSUYLIkn_h8CE6YxJVnDCuEJEKsR4mhUpUwVnmGWMrChN6RhcDHcb795aE2K-ca2v-8qcEEoFTTOGe-pyoJR3IXhT5I23lfRdjlG-05zvNOe_mnsYD_DWlqb7h8wXzw8vQ-YHNgOCBA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2233735941</pqid></control><display><type>article</type><title>A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory</title><source>Wiley Online Library Journals Frontfile Complete</source><source>EBSCOhost Business Source Complete</source><creator>Yang, Yan ; Hu, Junhua ; Liu, Yongmei ; Chen, Xiaohong</creator><creatorcontrib>Yang, Yan ; Hu, Junhua ; Liu, Yongmei ; Chen, Xiaohong</creatorcontrib><description>Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.</description><identifier>ISSN: 0266-4720</identifier><identifier>EISSN: 1468-0394</identifier><identifier>DOI: 10.1111/exsy.12377</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>Analogies ; decision support model ; Decision support systems ; Decision theory ; Diagnosis ; Fuzzy logic ; Fuzzy sets ; Health services ; medical diagnosis ; Modules ; Signs and symptoms ; Similarity ; three‐way decision theory ; treatment advice</subject><ispartof>Expert systems, 2019-06, Vol.36 (3), p.n/a</ispartof><rights>2019 John Wiley & Sons, Ltd</rights><rights>2019 John Wiley & Sons Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3497-a3263c51389343293f2a21777124d94a4f6246c02ac04659f54cf6414942b3353</citedby><cites>FETCH-LOGICAL-c3497-a3263c51389343293f2a21777124d94a4f6246c02ac04659f54cf6414942b3353</cites><orcidid>0000-0002-8879-4970</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fexsy.12377$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fexsy.12377$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Yang, Yan</creatorcontrib><creatorcontrib>Hu, Junhua</creatorcontrib><creatorcontrib>Liu, Yongmei</creatorcontrib><creatorcontrib>Chen, Xiaohong</creatorcontrib><title>A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory</title><title>Expert systems</title><description>Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.</description><subject>Analogies</subject><subject>decision support model</subject><subject>Decision support systems</subject><subject>Decision theory</subject><subject>Diagnosis</subject><subject>Fuzzy logic</subject><subject>Fuzzy sets</subject><subject>Health services</subject><subject>medical diagnosis</subject><subject>Modules</subject><subject>Signs and symptoms</subject><subject>Similarity</subject><subject>three‐way decision theory</subject><subject>treatment advice</subject><issn>0266-4720</issn><issn>1468-0394</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kLtOwzAUhi0EEqWw8ASW2JBSfIvdjFVVLlIlBkCCKXJth7pK4mA7KtlY2HlGnoSUVGLjLGf5_vPrfACcYzTB_VyZ99BNMKFCHIARZnyaIJqxQzBChPOECYKOwUkIG4QQFoKPwOcMVm0ZbWO8dRquu5W3GmqjbLCuhqFtGucjrJw2JSych5XRVskSaitfaxdsgLLWMHojY2XqCFcyGA13UVvZUnobrdkza2_M98fXVnZ_BXFtnO9OwVEhy2DO9nsMnq4Xj_PbZHl_czefLRNFWSYSSQmnKsV0mlFGSUYLIkn_h8CE6YxJVnDCuEJEKsR4mhUpUwVnmGWMrChN6RhcDHcb795aE2K-ca2v-8qcEEoFTTOGe-pyoJR3IXhT5I23lfRdjlG-05zvNOe_mnsYD_DWlqb7h8wXzw8vQ-YHNgOCBA</recordid><startdate>201906</startdate><enddate>201906</enddate><creator>Yang, Yan</creator><creator>Hu, Junhua</creator><creator>Liu, Yongmei</creator><creator>Chen, Xiaohong</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-8879-4970</orcidid></search><sort><creationdate>201906</creationdate><title>A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory</title><author>Yang, Yan ; Hu, Junhua ; Liu, Yongmei ; Chen, Xiaohong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3497-a3263c51389343293f2a21777124d94a4f6246c02ac04659f54cf6414942b3353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Analogies</topic><topic>decision support model</topic><topic>Decision support systems</topic><topic>Decision theory</topic><topic>Diagnosis</topic><topic>Fuzzy logic</topic><topic>Fuzzy sets</topic><topic>Health services</topic><topic>medical diagnosis</topic><topic>Modules</topic><topic>Signs and symptoms</topic><topic>Similarity</topic><topic>three‐way decision theory</topic><topic>treatment advice</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Yan</creatorcontrib><creatorcontrib>Hu, Junhua</creatorcontrib><creatorcontrib>Liu, Yongmei</creatorcontrib><creatorcontrib>Chen, Xiaohong</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Yan</au><au>Hu, Junhua</au><au>Liu, Yongmei</au><au>Chen, Xiaohong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory</atitle><jtitle>Expert systems</jtitle><date>2019-06</date><risdate>2019</risdate><volume>36</volume><issue>3</issue><epage>n/a</epage><issn>0266-4720</issn><eissn>1468-0394</eissn><abstract>Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/exsy.12377</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0002-8879-4970</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0266-4720 |
| ispartof | Expert systems, 2019-06, Vol.36 (3), p.n/a |
| issn | 0266-4720 1468-0394 |
| language | eng |
| recordid | cdi_proquest_journals_2233735941 |
| source | Wiley Online Library Journals Frontfile Complete; EBSCOhost Business Source Complete |
| subjects | Analogies decision support model Decision support systems Decision theory Diagnosis Fuzzy logic Fuzzy sets Health services medical diagnosis Modules Signs and symptoms Similarity three‐way decision theory treatment advice |
| title | A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T19%3A49%3A28IST&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=A%20multiperiod%20hybrid%20decision%20support%20model%20for%20medical%20diagnosis%20and%20treatment%20based%20on%20similarities%20and%20three%E2%80%90way%20decision%20theory&rft.jtitle=Expert%20systems&rft.au=Yang,%20Yan&rft.date=2019-06&rft.volume=36&rft.issue=3&rft.epage=n/a&rft.issn=0266-4720&rft.eissn=1468-0394&rft_id=info:doi/10.1111/exsy.12377&rft_dat=%3Cproquest_cross%3E2233735941%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=2233735941&rft_id=info:pmid/&rfr_iscdi=true |