LEADER 02775nam 2200649 450 001 9910464355003321 005 20200520144314.0 010 $a0-309-26805-2 035 $a(CKB)2670000000499682 035 $a(EBL)3379122 035 $a(SSID)ssj0001064982 035 $a(PQKBManifestationID)11613941 035 $a(PQKBTitleCode)TC0001064982 035 $a(PQKBWorkID)11060517 035 $a(PQKB)10024817 035 $a(MiAaPQ)EBC3379122 035 $a(Au-PeEL)EBL3379122 035 $a(CaPaEBR)ebr10863774 035 $a(OCoLC)865150972 035 $a(EXLCZ)992670000000499682 100 $a20131122h20132013 uy| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCollecting sexual orientation and gender identity data in electronic health records $eworkshop summary /$fJoe Alper, Monica N. Feit, and Jon Q. Sanders, rapporteurs ; Board on the Health of Select Populations, Institute of Medicine of the National Academies 210 1$aWashington, District of Columbia :$cNational Academies Press,$d[2013] 210 4$dİ2013 215 $a1 online resource (73 p.) 300 $aDescription based upon print version of record. 311 $a0-309-26804-4 320 $aIncludes bibliographical references (page 53). 327 $aIntroduction and overview -- Clinical rationale for collecting sexual orientation and gender identity data -- Federal perspective on the use of electronic health records to collect sexual orientation and gender indentity data -- Existing data collection practices in clinical settings -- Developing and implementing questions for collecting data on sexual orientation and gender identity -- Closing remarks. 606 $aMedical records$zUnited States$xData processing$vCongresses 606 $aMedical records$xGovernment policy$zUnited States$vCongresses 606 $aMedical records$xStandards$zUnited States$vCongresses 606 $aGender identity$zUnited States$vCongresses 606 $aSexual orientation$zUnited States$vCongresses 608 $aElectronic books. 615 0$aMedical records$xData processing 615 0$aMedical records$xGovernment policy 615 0$aMedical records$xStandards 615 0$aGender identity 615 0$aSexual orientation 676 $a610.285 702 $aAlper$b Joe 702 $aFeit$b Monica N. 702 $aSanders$b Jon Q. 712 02$aInstitute of Medicine (U.S.).$bBoard on the Health of Select Populations, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464355003321 996 $aCollecting sexual orientation and gender identity data in electronic health records$92442458 997 $aUNINA LEADER 01846nam 2200373 450 001 9910172607403321 005 20230420154142.0 010 $a1-5090-6597-0 035 $a(CKB)3710000001362013 035 $a(NjHacI)993710000001362013 035 $a(EXLCZ)993710000001362013 100 $a20230420d2017 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) /$fInstitute of Electrical and Electronics Engineers (IEEE) 210 1$aPiscataway, New Jersey :$cInstitute of Electrical and Electronics Engineers (IEEE),$d2017. 215 $a1 online resource (various pagings) $cillustrations 311 $a1-5090-6598-9 330 $aThe aim of the workshop is to provide a forum for researchers and practitioners to present and discuss new ideas, trends and results concerning applying ML to software quality evaluation We expect that the workshop will help in (1) validation of existing and exploring new applications of ML, (2) comparing their efficiency and effectiveness, both among other automated approaches and the human judgment, and (3) adapting ML approaches already used in other areas of science. 517 $a2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation 606 $aComputer software$xEvaluation$vCongresses 606 $aMachine learning$vCongresses 615 0$aComputer software$xEvaluation 615 0$aMachine learning 676 $a005 801 0$bNjHacI 801 1$bNjHacl 906 $aPROCEEDING 912 $a9910172607403321 996 $a2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)$92509045 997 $aUNINA