Longer-term growth prediction using GAUSS
In several areas of biomedicine, one needs to predict future measurements for a growing individual on the basis of longitudinal data. Here we consider the problem of estimating the values of a given measurement for a particular individual at T-T ∗ points in time, given T ∗ observations on that indiv...
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| Veröffentlicht in: | Computers in biology and medicine 1993-03, Vol.23 (2), p.149-154 |
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| container_title | Computers in biology and medicine |
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| creator | Schneiderman, Emet D. Willis, Stephen M. Kowalski, Charles J. Ten Have, Thomas R. |
| description | In several areas of biomedicine, one needs to predict future measurements for a growing individual on the basis of longitudinal data. Here we consider the problem of estimating the values of a given measurement for a particular individual at
T-T
∗
points in time, given
T
∗
observations on that individual, and all
T values for a sample of
N “similar” individuals. This extends our previous discussion [Schneiderman
et al., Comput. Biol. Med.
22, 181–188 (1992)], which was limited to the case
T
∗ = T − 1
, to longer-term predictions. We again make a user-friendly GAUSS program available to perform the associated computations. Examples illustrating the use of the program and the accuracy of the predictions it provides are included. |
| doi_str_mv | 10.1016/0010-4825(93)90146-R |
| format | Article |
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T-T
∗
points in time, given
T
∗
observations on that individual, and all
T values for a sample of
N “similar” individuals. This extends our previous discussion [Schneiderman
et al., Comput. Biol. Med.
22, 181–188 (1992)], which was limited to the case
T
∗ = T − 1
, to longer-term predictions. We again make a user-friendly GAUSS program available to perform the associated computations. Examples illustrating the use of the program and the accuracy of the predictions it provides are included.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/0010-4825(93)90146-R</identifier><identifier>PMID: 8513666</identifier><identifier>CODEN: CBMDAW</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algorithms ; Animals ; Biological and medical sciences ; Computerized, statistical medical data processing and models in biomedicine ; Forecasting ; Growth ; Longitudinal studies ; Macaca mulatta ; Male ; Mandible - growth & development ; Medical management aid. Diagnosis aid ; Medical sciences ; Models, Biological ; PC program ; Polynomials ; Prediction ; Software ; Time Factors</subject><ispartof>Computers in biology and medicine, 1993-03, Vol.23 (2), p.149-154</ispartof><rights>1993</rights><rights>1993 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c432t-8a931408d6644fd0bbe828bb68f383268513859ef7e631769c971da3a0ac53b23</citedby><cites>FETCH-LOGICAL-c432t-8a931408d6644fd0bbe828bb68f383268513859ef7e631769c971da3a0ac53b23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/0010-4825(93)90146-R$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=4735120$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/8513666$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schneiderman, Emet D.</creatorcontrib><creatorcontrib>Willis, Stephen M.</creatorcontrib><creatorcontrib>Kowalski, Charles J.</creatorcontrib><creatorcontrib>Ten Have, Thomas R.</creatorcontrib><title>Longer-term growth prediction using GAUSS</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>In several areas of biomedicine, one needs to predict future measurements for a growing individual on the basis of longitudinal data. Here we consider the problem of estimating the values of a given measurement for a particular individual at
T-T
∗
points in time, given
T
∗
observations on that individual, and all
T values for a sample of
N “similar” individuals. This extends our previous discussion [Schneiderman
et al., Comput. Biol. Med.
22, 181–188 (1992)], which was limited to the case
T
∗ = T − 1
, to longer-term predictions. We again make a user-friendly GAUSS program available to perform the associated computations. Examples illustrating the use of the program and the accuracy of the predictions it provides are included.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Biological and medical sciences</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Forecasting</subject><subject>Growth</subject><subject>Longitudinal studies</subject><subject>Macaca mulatta</subject><subject>Male</subject><subject>Mandible - growth & development</subject><subject>Medical management aid. Diagnosis aid</subject><subject>Medical sciences</subject><subject>Models, Biological</subject><subject>PC program</subject><subject>Polynomials</subject><subject>Prediction</subject><subject>Software</subject><subject>Time Factors</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1993</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kF1LwzAUhoMoc07_gUIvRNxF9aRJ0-RGGEOnMBA2dx3SNJ2RfsykVfz3tq7s0qtzcZ73fDwIXWK4w4DZPQCGkPIovhVkKgBTFq6O0BjzRIQQE3qMxgfkFJ15_wEAFAiM0IjHmDDGxmi6rKutcWFjXBlsXf3dvAc7ZzKrG1tXQetttQ0Ws816fY5OclV4czHUCdo8Pb7Nn8Pl6-JlPluGmpKoCbkSBFPgGWOU5hmkqeERT1PGc8JJxPrNPBYmTwwjOGFCiwRniihQOiZpRCboZj935-rP1vhGltZrUxSqMnXrZRInIu6O70C6B7WrvXcmlztnS-V-JAbZG5L9-7J_Xwoi_wzJVRe7Gua3aWmyQ2hQ0vWvh77yWhW5U5W2_oDRhMQ4gg572GOmc_FljZNeW1PpTp0zupFZbf-_4xdfD38_</recordid><startdate>19930301</startdate><enddate>19930301</enddate><creator>Schneiderman, Emet D.</creator><creator>Willis, Stephen M.</creator><creator>Kowalski, Charles J.</creator><creator>Ten Have, Thomas R.</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><scope>IQODW</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>7X8</scope></search><sort><creationdate>19930301</creationdate><title>Longer-term growth prediction using GAUSS</title><author>Schneiderman, Emet D. ; Willis, Stephen M. ; Kowalski, Charles J. ; Ten Have, Thomas R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c432t-8a931408d6644fd0bbe828bb68f383268513859ef7e631769c971da3a0ac53b23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Biological and medical sciences</topic><topic>Computerized, statistical medical data processing and models in biomedicine</topic><topic>Forecasting</topic><topic>Growth</topic><topic>Longitudinal studies</topic><topic>Macaca mulatta</topic><topic>Male</topic><topic>Mandible - growth & development</topic><topic>Medical management aid. Diagnosis aid</topic><topic>Medical sciences</topic><topic>Models, Biological</topic><topic>PC program</topic><topic>Polynomials</topic><topic>Prediction</topic><topic>Software</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schneiderman, Emet D.</creatorcontrib><creatorcontrib>Willis, Stephen M.</creatorcontrib><creatorcontrib>Kowalski, Charles J.</creatorcontrib><creatorcontrib>Ten Have, Thomas R.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schneiderman, Emet D.</au><au>Willis, Stephen M.</au><au>Kowalski, Charles J.</au><au>Ten Have, Thomas R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Longer-term growth prediction using GAUSS</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>1993-03-01</date><risdate>1993</risdate><volume>23</volume><issue>2</issue><spage>149</spage><epage>154</epage><pages>149-154</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><coden>CBMDAW</coden><abstract>In several areas of biomedicine, one needs to predict future measurements for a growing individual on the basis of longitudinal data. Here we consider the problem of estimating the values of a given measurement for a particular individual at
T-T
∗
points in time, given
T
∗
observations on that individual, and all
T values for a sample of
N “similar” individuals. This extends our previous discussion [Schneiderman
et al., Comput. Biol. Med.
22, 181–188 (1992)], which was limited to the case
T
∗ = T − 1
, to longer-term predictions. We again make a user-friendly GAUSS program available to perform the associated computations. Examples illustrating the use of the program and the accuracy of the predictions it provides are included.</abstract><cop>Oxford</cop><cop>New York, NY</cop><pub>Elsevier Ltd</pub><pmid>8513666</pmid><doi>10.1016/0010-4825(93)90146-R</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Algorithms Animals Biological and medical sciences Computerized, statistical medical data processing and models in biomedicine Forecasting Growth Longitudinal studies Macaca mulatta Male Mandible - growth & development Medical management aid. Diagnosis aid Medical sciences Models, Biological PC program Polynomials Prediction Software Time Factors |
| title | Longer-term growth prediction using GAUSS |
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