Decision support of inspired oxygen selection based on Bayesian learning of pulmonary gas exchange parameters
To investigate if the real-time Bayesian learning of physiological model parameters can be used to support and improve the selection of inspired oxygen fraction. Supporting the selection of inspired oxygen fraction relies on predictions of arterial oxygen saturation. The efficacy of using these pred...
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| Veröffentlicht in: | Artificial intelligence in medicine 2005-05, Vol.34 (1), p.53-63 |
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| container_title | Artificial intelligence in medicine |
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| creator | Murley, David Rees, Stephen Rasmussen, Bodil Andreassen, Steen |
| description | To investigate if the real-time Bayesian learning of physiological model parameters can be used to support and improve the selection of inspired oxygen fraction.
Supporting the selection of inspired oxygen fraction relies on predictions of arterial oxygen saturation. The efficacy of using these predictions to select inspired oxygen was tested retrospectively in a system for estimating gas exchange parameters of the lung (Automatic Lung Parameter Estimator, ALPE). For the predictions to offer effective decision support they need to be accurate and above all safe. These qualities were tested with data from 16 post-operative cardiac patients, using two different tests. The aim of the first test was to assess retrospectively if the predictions could have supported clinical decisions. The second test sought to establish if the predictions could support improving the efficiency of inspired oxygen selection during an ALPE oxygen titration.
The predictions were found to be reasonably accurate, and most importantly safe in both of the tests.
The method described can be used to support the selection of inspired oxygen fraction, and it has the potential to improve the efficiency of inspired oxygen selection during an oxygen titration. |
| doi_str_mv | 10.1016/j.artmed.2004.07.012 |
| format | Article |
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Supporting the selection of inspired oxygen fraction relies on predictions of arterial oxygen saturation. The efficacy of using these predictions to select inspired oxygen was tested retrospectively in a system for estimating gas exchange parameters of the lung (Automatic Lung Parameter Estimator, ALPE). For the predictions to offer effective decision support they need to be accurate and above all safe. These qualities were tested with data from 16 post-operative cardiac patients, using two different tests. The aim of the first test was to assess retrospectively if the predictions could have supported clinical decisions. The second test sought to establish if the predictions could support improving the efficiency of inspired oxygen selection during an ALPE oxygen titration.
The predictions were found to be reasonably accurate, and most importantly safe in both of the tests.
The method described can be used to support the selection of inspired oxygen fraction, and it has the potential to improve the efficiency of inspired oxygen selection during an oxygen titration.</description><identifier>ISSN: 0933-3657</identifier><identifier>EISSN: 1873-2860</identifier><identifier>DOI: 10.1016/j.artmed.2004.07.012</identifier><identifier>PMID: 15885566</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Bayes Theorem ; Bayesian learning ; Computer applications ; Decision support ; Decision support systems ; Decision Support Techniques ; Humans ; Medicine ; Modelling ; Models, Biological ; Monitoring, Physiologic - instrumentation ; Oxygen - blood ; Oxygen saturation ; Physiological modelling ; Pulmonary gas exchange ; Pulmonary Gas Exchange - physiology ; Retrospective Studies</subject><ispartof>Artificial intelligence in medicine, 2005-05, Vol.34 (1), p.53-63</ispartof><rights>2004 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c422t-d014aec892de635c5f2598f8daef6da679ba1d40dc5ec17713e10e400253b39f3</citedby><cites>FETCH-LOGICAL-c422t-d014aec892de635c5f2598f8daef6da679ba1d40dc5ec17713e10e400253b39f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.artmed.2004.07.012$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15885566$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Murley, David</creatorcontrib><creatorcontrib>Rees, Stephen</creatorcontrib><creatorcontrib>Rasmussen, Bodil</creatorcontrib><creatorcontrib>Andreassen, Steen</creatorcontrib><title>Decision support of inspired oxygen selection based on Bayesian learning of pulmonary gas exchange parameters</title><title>Artificial intelligence in medicine</title><addtitle>Artif Intell Med</addtitle><description>To investigate if the real-time Bayesian learning of physiological model parameters can be used to support and improve the selection of inspired oxygen fraction.
Supporting the selection of inspired oxygen fraction relies on predictions of arterial oxygen saturation. The efficacy of using these predictions to select inspired oxygen was tested retrospectively in a system for estimating gas exchange parameters of the lung (Automatic Lung Parameter Estimator, ALPE). For the predictions to offer effective decision support they need to be accurate and above all safe. These qualities were tested with data from 16 post-operative cardiac patients, using two different tests. The aim of the first test was to assess retrospectively if the predictions could have supported clinical decisions. The second test sought to establish if the predictions could support improving the efficiency of inspired oxygen selection during an ALPE oxygen titration.
The predictions were found to be reasonably accurate, and most importantly safe in both of the tests.
The method described can be used to support the selection of inspired oxygen fraction, and it has the potential to improve the efficiency of inspired oxygen selection during an oxygen titration.</description><subject>Bayes Theorem</subject><subject>Bayesian learning</subject><subject>Computer applications</subject><subject>Decision support</subject><subject>Decision support systems</subject><subject>Decision Support Techniques</subject><subject>Humans</subject><subject>Medicine</subject><subject>Modelling</subject><subject>Models, Biological</subject><subject>Monitoring, Physiologic - instrumentation</subject><subject>Oxygen - blood</subject><subject>Oxygen saturation</subject><subject>Physiological modelling</subject><subject>Pulmonary gas exchange</subject><subject>Pulmonary Gas Exchange - physiology</subject><subject>Retrospective Studies</subject><issn>0933-3657</issn><issn>1873-2860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1v1DAQhi0EotvCP0DIJ24J4-_kggQFClIlLnC2vPZk8Spxgp1U3X9Pol1xpHOxNPO8Y2keQt4wqBkw_f5YuzwPGGoOIGswNTD-jOxYY0TFGw3PyQ5aISqhlbki16UcAcBIpl-SK6aaRimtd2T4jD6WOCZalmka80zHjsZUppgx0PHxdMB1hD36eYP2rmztRD-5E5boEu3R5RTTYctNSz-MyeUTPbhC8dH_dumAdHLZDThjLq_Ii871BV9f3hvy6-uXn7ffqvsfd99vP95XXnI-VwGYdOiblgfUQnnVcdU2XRMcdjo4bdq9Y0FC8Ao9M4YJZIASgCuxF20nbsi7894pj38WLLMdYvHY9y7huBSrTQMtF-2ToDJaMgb6SZC1cq2GraA8gz6PpWTs7JTjsN7EMrCbOHu0Z3F2E2fB2FXcGnt72b_st9m_0MXUCnw4A7je7SFitsVHTB7DasrPNozx_z_8BeZKrV0</recordid><startdate>20050501</startdate><enddate>20050501</enddate><creator>Murley, David</creator><creator>Rees, Stephen</creator><creator>Rasmussen, Bodil</creator><creator>Andreassen, Steen</creator><general>Elsevier B.V</general><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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>E3H</scope><scope>F2A</scope><scope>7X8</scope></search><sort><creationdate>20050501</creationdate><title>Decision support of inspired oxygen selection based on Bayesian learning of pulmonary gas exchange parameters</title><author>Murley, David ; Rees, Stephen ; Rasmussen, Bodil ; Andreassen, Steen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-d014aec892de635c5f2598f8daef6da679ba1d40dc5ec17713e10e400253b39f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Bayes Theorem</topic><topic>Bayesian learning</topic><topic>Computer applications</topic><topic>Decision support</topic><topic>Decision support systems</topic><topic>Decision Support Techniques</topic><topic>Humans</topic><topic>Medicine</topic><topic>Modelling</topic><topic>Models, Biological</topic><topic>Monitoring, Physiologic - instrumentation</topic><topic>Oxygen - blood</topic><topic>Oxygen saturation</topic><topic>Physiological modelling</topic><topic>Pulmonary gas exchange</topic><topic>Pulmonary Gas Exchange - physiology</topic><topic>Retrospective Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Murley, David</creatorcontrib><creatorcontrib>Rees, Stephen</creatorcontrib><creatorcontrib>Rasmussen, Bodil</creatorcontrib><creatorcontrib>Andreassen, Steen</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>MEDLINE - Academic</collection><jtitle>Artificial intelligence in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Murley, David</au><au>Rees, Stephen</au><au>Rasmussen, Bodil</au><au>Andreassen, Steen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Decision support of inspired oxygen selection based on Bayesian learning of pulmonary gas exchange parameters</atitle><jtitle>Artificial intelligence in medicine</jtitle><addtitle>Artif Intell Med</addtitle><date>2005-05-01</date><risdate>2005</risdate><volume>34</volume><issue>1</issue><spage>53</spage><epage>63</epage><pages>53-63</pages><issn>0933-3657</issn><eissn>1873-2860</eissn><abstract>To investigate if the real-time Bayesian learning of physiological model parameters can be used to support and improve the selection of inspired oxygen fraction.
Supporting the selection of inspired oxygen fraction relies on predictions of arterial oxygen saturation. The efficacy of using these predictions to select inspired oxygen was tested retrospectively in a system for estimating gas exchange parameters of the lung (Automatic Lung Parameter Estimator, ALPE). For the predictions to offer effective decision support they need to be accurate and above all safe. These qualities were tested with data from 16 post-operative cardiac patients, using two different tests. The aim of the first test was to assess retrospectively if the predictions could have supported clinical decisions. The second test sought to establish if the predictions could support improving the efficiency of inspired oxygen selection during an ALPE oxygen titration.
The predictions were found to be reasonably accurate, and most importantly safe in both of the tests.
The method described can be used to support the selection of inspired oxygen fraction, and it has the potential to improve the efficiency of inspired oxygen selection during an oxygen titration.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>15885566</pmid><doi>10.1016/j.artmed.2004.07.012</doi><tpages>11</tpages></addata></record> |
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| ispartof | Artificial intelligence in medicine, 2005-05, Vol.34 (1), p.53-63 |
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| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Bayes Theorem Bayesian learning Computer applications Decision support Decision support systems Decision Support Techniques Humans Medicine Modelling Models, Biological Monitoring, Physiologic - instrumentation Oxygen - blood Oxygen saturation Physiological modelling Pulmonary gas exchange Pulmonary Gas Exchange - physiology Retrospective Studies |
| title | Decision support of inspired oxygen selection based on Bayesian learning of pulmonary gas exchange parameters |
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