The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes
Diabetes may result in some complications and increase the risk of many serious health problems. The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patient...
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| Veröffentlicht in: | BioMed research international 2016-01, Vol.2016 (2016), p.1-6 |
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| creator | Li, Quanzhong Sun, Changqing Wei, Fenfen Wang, Yannian |
| description | Diabetes may result in some complications and increase the risk of many serious health problems. The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patients. For this purpose, an improved grey GM (1, 1) model is applied to predict blood glucose with a small amount of data, and especially in terms of improved smoothness it can get higher prediction accuracy. The original data of blood glucose of type 2 diabetes is acquired by CGMS. Then the prediction model is established. Finally, 50 cases of blood glucose from the Henan Province People’s Hospital are predicted in 5, 10, 15, 20, 25, and 30 minutes, respectively, in advance to verify the prediction model. The prediction result of blood glucose is evaluated by the EGA, MSE, and MAE. Particularly, the prediction results of postprandial blood glucose are presented and analyzed. The result shows that the improved grey GM (1, 1) model has excellent performance in postprandial blood glucose prediction. |
| doi_str_mv | 10.1155/2016/6837052 |
| format | Article |
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The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patients. For this purpose, an improved grey GM (1, 1) model is applied to predict blood glucose with a small amount of data, and especially in terms of improved smoothness it can get higher prediction accuracy. The original data of blood glucose of type 2 diabetes is acquired by CGMS. Then the prediction model is established. Finally, 50 cases of blood glucose from the Henan Province People’s Hospital are predicted in 5, 10, 15, 20, 25, and 30 minutes, respectively, in advance to verify the prediction model. The prediction result of blood glucose is evaluated by the EGA, MSE, and MAE. Particularly, the prediction results of postprandial blood glucose are presented and analyzed. The result shows that the improved grey GM (1, 1) model has excellent performance in postprandial blood glucose prediction.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2016/6837052</identifier><identifier>PMID: 27314034</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Analysis ; Blood Glucose - metabolism ; Blood Glucose Self-Monitoring - methods ; Blood sugar ; Computer Simulation ; Diabetes ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetes Mellitus, Type 2 - physiopathology ; Diagnosis, Computer-Assisted - methods ; Forecasts and trends ; Glucose ; Growth models ; Humans ; Kalman filters ; Least-Squares Analysis ; Metabolic Clearance Rate ; Models, Biological ; Models, Statistical ; Neural networks ; Physicians ; Postprandial Period ; Reproducibility of Results ; Science ; Sensitivity and Specificity ; Type 2 diabetes</subject><ispartof>BioMed research international, 2016-01, Vol.2016 (2016), p.1-6</ispartof><rights>Copyright © 2016 Yannian Wang et al.</rights><rights>COPYRIGHT 2016 John Wiley & Sons, Inc.</rights><rights>Copyright © 2016 Yannian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2016 Yannian Wang et al. 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-6336b6dce6f4f6641d318ae45f411c0e7310d54d6fd2335b3405a5625e38d8f23</citedby><cites>FETCH-LOGICAL-c499t-6336b6dce6f4f6641d318ae45f411c0e7310d54d6fd2335b3405a5625e38d8f23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893588/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4893588/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27922,27923,53789,53791</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27314034$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Saisho, Yoshifumi</contributor><creatorcontrib>Li, Quanzhong</creatorcontrib><creatorcontrib>Sun, Changqing</creatorcontrib><creatorcontrib>Wei, Fenfen</creatorcontrib><creatorcontrib>Wang, Yannian</creatorcontrib><title>The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Diabetes may result in some complications and increase the risk of many serious health problems. The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patients. For this purpose, an improved grey GM (1, 1) model is applied to predict blood glucose with a small amount of data, and especially in terms of improved smoothness it can get higher prediction accuracy. The original data of blood glucose of type 2 diabetes is acquired by CGMS. Then the prediction model is established. Finally, 50 cases of blood glucose from the Henan Province People’s Hospital are predicted in 5, 10, 15, 20, 25, and 30 minutes, respectively, in advance to verify the prediction model. The prediction result of blood glucose is evaluated by the EGA, MSE, and MAE. Particularly, the prediction results of postprandial blood glucose are presented and analyzed. The result shows that the improved grey GM (1, 1) model has excellent performance in postprandial blood glucose prediction.</description><subject>Accuracy</subject><subject>Analysis</subject><subject>Blood Glucose - metabolism</subject><subject>Blood Glucose Self-Monitoring - methods</subject><subject>Blood sugar</subject><subject>Computer Simulation</subject><subject>Diabetes</subject><subject>Diabetes Mellitus, Type 2 - diagnosis</subject><subject>Diabetes Mellitus, Type 2 - physiopathology</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Forecasts and trends</subject><subject>Glucose</subject><subject>Growth models</subject><subject>Humans</subject><subject>Kalman filters</subject><subject>Least-Squares Analysis</subject><subject>Metabolic Clearance Rate</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Neural networks</subject><subject>Physicians</subject><subject>Postprandial Period</subject><subject>Reproducibility of Results</subject><subject>Science</subject><subject>Sensitivity and Specificity</subject><subject>Type 2 diabetes</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNks9LHDEUx4fSUsV667kEerHoan4ncymIbbeCUinbc8hOXtzI7GRMZiz735tl19X2ZC4JvE8-eY9vquojwaeECHFGMZFnUjOFBX1T7VNG-EQSTt7uzoztVYc53-GyNJG4lu-rPapKETO-X7nZAtBvyGBTs0DRo8tln-IDODRNsELTa3REThD5gq6jgxYNEd0kcKEZ0FAu3sQ89Ml2LtgWTduxiRlQ6NBs1QOi6Fuwcxggf6jeedtmONzuB9WfH99nFz8nV7-mlxfnV5OG1_UwkYzJuXQNSM-9lJw4RrQFLjwnpMFQmsZOcCe9o4yJOeNYWCGpAKad9pQdVF833n6cL6GIuiHZ1vQpLG1amWiD-bfShYW5jQ-G65oJrYvgaCtI8X6EPJhlyA20re0gjtkQVSutaE1UQT__h97FMXVlvDXFtFAK62fq1rZgQudjebdZS825oFiVDDEp1MmGalLMOYHftUywWeds1jmbbc4F__RyzB38lGoBjjfAInTO_g2v1EFhwNsXdK3Lh2GPkAS1Gg</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Li, Quanzhong</creator><creator>Sun, Changqing</creator><creator>Wei, Fenfen</creator><creator>Wang, Yannian</creator><general>Hindawi Publishing Corporation</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160101</creationdate><title>The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes</title><author>Li, Quanzhong ; Sun, Changqing ; Wei, Fenfen ; Wang, Yannian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c499t-6336b6dce6f4f6641d318ae45f411c0e7310d54d6fd2335b3405a5625e38d8f23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Accuracy</topic><topic>Analysis</topic><topic>Blood Glucose - metabolism</topic><topic>Blood Glucose Self-Monitoring - methods</topic><topic>Blood sugar</topic><topic>Computer Simulation</topic><topic>Diabetes</topic><topic>Diabetes Mellitus, Type 2 - diagnosis</topic><topic>Diabetes Mellitus, Type 2 - physiopathology</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Forecasts and trends</topic><topic>Glucose</topic><topic>Growth models</topic><topic>Humans</topic><topic>Kalman filters</topic><topic>Least-Squares Analysis</topic><topic>Metabolic Clearance Rate</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Neural networks</topic><topic>Physicians</topic><topic>Postprandial Period</topic><topic>Reproducibility of Results</topic><topic>Science</topic><topic>Sensitivity and Specificity</topic><topic>Type 2 diabetes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Quanzhong</creatorcontrib><creatorcontrib>Sun, Changqing</creatorcontrib><creatorcontrib>Wei, Fenfen</creatorcontrib><creatorcontrib>Wang, Yannian</creatorcontrib><collection>الدوريات العلمية والإحصائية - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Quanzhong</au><au>Sun, Changqing</au><au>Wei, Fenfen</au><au>Wang, Yannian</au><au>Saisho, Yoshifumi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2016-01-01</date><risdate>2016</risdate><volume>2016</volume><issue>2016</issue><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Diabetes may result in some complications and increase the risk of many serious health problems. The purpose of clinical treatment is to carefully manage the blood glucose concentration. If the blood glucose concentration is predicted, treatments can be taken in advance to reduce the harm to patients. For this purpose, an improved grey GM (1, 1) model is applied to predict blood glucose with a small amount of data, and especially in terms of improved smoothness it can get higher prediction accuracy. The original data of blood glucose of type 2 diabetes is acquired by CGMS. Then the prediction model is established. Finally, 50 cases of blood glucose from the Henan Province People’s Hospital are predicted in 5, 10, 15, 20, 25, and 30 minutes, respectively, in advance to verify the prediction model. The prediction result of blood glucose is evaluated by the EGA, MSE, and MAE. Particularly, the prediction results of postprandial blood glucose are presented and analyzed. The result shows that the improved grey GM (1, 1) model has excellent performance in postprandial blood glucose prediction.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>27314034</pmid><doi>10.1155/2016/6837052</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Analysis Blood Glucose - metabolism Blood Glucose Self-Monitoring - methods Blood sugar Computer Simulation Diabetes Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - physiopathology Diagnosis, Computer-Assisted - methods Forecasts and trends Glucose Growth models Humans Kalman filters Least-Squares Analysis Metabolic Clearance Rate Models, Biological Models, Statistical Neural networks Physicians Postprandial Period Reproducibility of Results Science Sensitivity and Specificity Type 2 diabetes |
| title | The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes |
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