Expert system support using Bayesian belief networks in the prognosis of head-injured patients of the ICU
The present study concerns the construction and operation of a Bayesian analytical system, namely a Bayesian belief network (BBN) for the prognosis at 24 h of head-injured patients of the intensive care unit. The construction of a BBN incorporates the maintenance of a large database including all th...
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| Veröffentlicht in: | Medical informatics 1998, Vol.23 (1), p.1-18 |
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| container_title | Medical informatics |
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| creator | Nikiforidis, G. C. Sakellaropoulos, G. C. |
| description | The present study concerns the construction and operation of a Bayesian analytical system, namely a Bayesian belief network (BBN) for the prognosis at 24 h of head-injured patients of the intensive care unit. The construction of a BBN incorporates the maintenance of a large database including all the critical variables corresponding to the specific clinical domain. This database is processed to provide the necessary libraries of conditional probability values. BBNs permit the combination of prognostic evidence in a cumulative manner and provide a quantitative measure of certainty in the final decision. The user views the changes at each step, thus being capable of deciding upon the necessary pieces of information in order to reach a certain belief threshold. The system produces results that are compatible with the opinions of medical experts regarding the prognosis of patients exhibiting certain patterns of clinical or laboratory data. |
| doi_str_mv | 10.3109/14639239809001387 |
| format | Article |
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C.</creatorcontrib><creatorcontrib>Sakellaropoulos, G. C.</creatorcontrib><title>Expert system support using Bayesian belief networks in the prognosis of head-injured patients of the ICU</title><title>Medical informatics</title><addtitle>Med Inform (Lond)</addtitle><description>The present study concerns the construction and operation of a Bayesian analytical system, namely a Bayesian belief network (BBN) for the prognosis at 24 h of head-injured patients of the intensive care unit. The construction of a BBN incorporates the maintenance of a large database including all the critical variables corresponding to the specific clinical domain. This database is processed to provide the necessary libraries of conditional probability values. BBNs permit the combination of prognostic evidence in a cumulative manner and provide a quantitative measure of certainty in the final decision. The user views the changes at each step, thus being capable of deciding upon the necessary pieces of information in order to reach a certain belief threshold. The system produces results that are compatible with the opinions of medical experts regarding the prognosis of patients exhibiting certain patterns of clinical or laboratory data.</description><subject>Adult</subject><subject>Artificial Intelligence</subject><subject>Bayes Theorem</subject><subject>Belief networks</subject><subject>Brain Injuries - classification</subject><subject>Brain Injuries - diagnosis</subject><subject>Brain Injuries - mortality</subject><subject>Databases, Factual</subject><subject>Decision Making, Computer-Assisted</subject><subject>Expert system</subject><subject>Expert Systems</subject><subject>Female</subject><subject>Glasgow Coma Scale</subject><subject>Head injury</subject><subject>Hospital Mortality</subject><subject>Humans</subject><subject>Intensive Care Units</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Neural Networks (Computer)</subject><subject>Probability</subject><subject>Prognosis</subject><subject>Software</subject><issn>1463-9238</issn><issn>0307-7640</issn><issn>1464-5238</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMtu3CAUhlHVKLf2AbqoxKo7J-ZiG9RumlFuUqRsmrXFwCHD1AYXsJJ5-zCZUaUoalfA-S86fAh9IfUZI7U8J7xlkjIpalnXhInuAzouM141lImPr3dWFYM4QicprYunFR09RIeyJaLt5DFyl88TxIzTJmUYcZqnKZTnnJx_xBdqA8kpj5cwOLDYQ34K8XfCzuO8AjzF8OhDcgkHi1egTOX8eo5g8KSyA59fha3zdvHwCR1YNST4vD9P0cPV5a_FTXV3f327-HlXac6aXGltiBSNUYYx0EZQSjqjNOdW8aZtam1oV77RGNFQueRSA3AumNSCG2MtZafo2663bPdnhpT70SUNw6A8hDn1nZS0qVlbjGRn1DGkFMH2U3Sjipue1P0Wb_8Ob8l83ZfPyxHM38SeZ9F_7HTnbYijKrgG02e1GUK0UXnt0rb63_Xf38QL0yGvtIrQr8McfeH2n-VeAJZMnB4</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Nikiforidis, G. 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C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c435t-ccd1985dad33ecd82217dac44fa45650cd270165d8529b49cee44839c84ddff23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Adult</topic><topic>Artificial Intelligence</topic><topic>Bayes Theorem</topic><topic>Belief networks</topic><topic>Brain Injuries - classification</topic><topic>Brain Injuries - diagnosis</topic><topic>Brain Injuries - mortality</topic><topic>Databases, Factual</topic><topic>Decision Making, Computer-Assisted</topic><topic>Expert system</topic><topic>Expert Systems</topic><topic>Female</topic><topic>Glasgow Coma Scale</topic><topic>Head injury</topic><topic>Hospital Mortality</topic><topic>Humans</topic><topic>Intensive Care Units</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Neural Networks (Computer)</topic><topic>Probability</topic><topic>Prognosis</topic><topic>Software</topic><toplevel>online_resources</toplevel><creatorcontrib>Nikiforidis, G. C.</creatorcontrib><creatorcontrib>Sakellaropoulos, G. C.</creatorcontrib><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>Medical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nikiforidis, G. C.</au><au>Sakellaropoulos, G. C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Expert system support using Bayesian belief networks in the prognosis of head-injured patients of the ICU</atitle><jtitle>Medical informatics</jtitle><addtitle>Med Inform (Lond)</addtitle><date>1998</date><risdate>1998</risdate><volume>23</volume><issue>1</issue><spage>1</spage><epage>18</epage><pages>1-18</pages><issn>1463-9238</issn><issn>0307-7640</issn><eissn>1464-5238</eissn><abstract>The present study concerns the construction and operation of a Bayesian analytical system, namely a Bayesian belief network (BBN) for the prognosis at 24 h of head-injured patients of the intensive care unit. The construction of a BBN incorporates the maintenance of a large database including all the critical variables corresponding to the specific clinical domain. This database is processed to provide the necessary libraries of conditional probability values. BBNs permit the combination of prognostic evidence in a cumulative manner and provide a quantitative measure of certainty in the final decision. The user views the changes at each step, thus being capable of deciding upon the necessary pieces of information in order to reach a certain belief threshold. The system produces results that are compatible with the opinions of medical experts regarding the prognosis of patients exhibiting certain patterns of clinical or laboratory data.</abstract><cop>England</cop><pub>Informa UK Ltd</pub><pmid>9618679</pmid><doi>10.3109/14639239809001387</doi><tpages>18</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1463-9238 |
| ispartof | Medical informatics, 1998, Vol.23 (1), p.1-18 |
| issn | 1463-9238 0307-7640 1464-5238 |
| language | eng |
| recordid | cdi_informahealthcare_journals_10_3109_14639239809001387 |
| source | MEDLINE; Taylor & Francis Medical Library - CRKN; Access via Taylor & Francis |
| subjects | Adult Artificial Intelligence Bayes Theorem Belief networks Brain Injuries - classification Brain Injuries - diagnosis Brain Injuries - mortality Databases, Factual Decision Making, Computer-Assisted Expert system Expert Systems Female Glasgow Coma Scale Head injury Hospital Mortality Humans Intensive Care Units Male Middle Aged Neural Networks (Computer) Probability Prognosis Software |
| title | Expert system support using Bayesian belief networks in the prognosis of head-injured patients of the ICU |
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