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QHCI 1 M l u u I I Lf 1 1 Nomngmcvoup m mg 0 19 a","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/9fd2ba21-1e23-4be2-b944-328f489f4741/track.jpg"}}},{"index":4,"time":988720,"duration":112760,"relevance":-1,"hit":false,"text":"EU Question Why is it so important to know such structures QHCI Winterhaller Cr Widera Pr Krasnogor N 2014 JEPETTOV a Cy oscape plugm for gene se ennchment and lopoluglcal o anaIVSIs based on umeractlon networks Blomiormatlcs 30 I7J 1029 1030 dOI 101093 blomf ormatlcs bn732 mmngu Gvouu u 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6130dd29-9fa8-48b4-9a46-3942fa4e3af8/track.jpg"}}},{"index":5,"time":1101480,"duration":41080,"relevance":-1,"hit":false,"text":"FOR FAIR SELECTION EVERYBODY HAS TO TAKE THE SAME 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report is the most imgortant medium 9H0 cm Radiologischer Befund Kurnnammw SI 9 SNV Fugusl llung Unlenuchung Thorax eme Ebene Ivegend Special Words SB BewegmgsaneVakle Zusland nach Schacelmmxrauma La ng u a ge M ix U Co m um uvurxemmuu keme dkule Slauunguemmn Fragllches Inllll al pmahular h m UF RW Evgass h A b b rey I atIO n S Zustana at Amaoe emes ET me Sp IZO ca 5cm clamaw dev Bv umanon my sonhoxop posmomcn ZVK ubov rL dIL SpwlzL m Flu am GIL VCS KLIH Hmevs um Prmmnomo a Dene Rezessushe Mu koHog mcn Gruf scn Holzinger A Generhofer R Erra h M 2007 Semannsche Informationsextraktlon In medlzlmschen Informatlonssystemen Informank Spektrum 30 2 59 78 mum Gvoun 17 m9 0 1953","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/27284237-fb75-4340-a31f-74ecf81814bd/track.jpg"}}},{"index":10,"time":1357840,"duration":17680,"relevance":-1,"hit":false,"text":"TU Example Chess Game versus Natural Language GHQ cm http stanford edu mplech cszzl apps deepslue html Hokmg v amp 5 mg 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/77300f41-333b-4d87-b72a-6096bdcedf12/track.jpg"}}},{"index":11,"time":1375520,"duration":15800,"relevance":-1,"hit":false,"text":"TU Example Chess Game versus Natural Language QHCF cm http stanford edu mplech cszzl apps deepslue html Hokmgnv Gvouv 3 709 04903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/0cbc64e6-8e73-4db6-9d59-8bc60c88046a/track.jpg"}}},{"index":12,"time":1391320,"duration":138080,"relevance":-1,"hit":false,"text":"TU German Example Synonymity and Ambiguity 9HCI cm Lhil fiwwme e n omum an ux 1 p a LA 1 MM 14 9 1 1L 2 w L__A LL 7 U r L JV L gal P1 7 w a Mr I v 2 A Lu 1 1 L J R7 1 iv 5 1 1 L l 1 M 7 xc J 1 5 1 x 0 Dag mo mnmnmmn qu w w die Antrumschleimhaut ist durch Lymphozyten infiltriert ymphozyt re Infiltration der Antrummukosa Lymphoyteninfiltration der Magenschleimhaut im Antrumbereich Hekmgtv Gvoup 9 me 0490","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/e96ce777-7079-4d1c-839f-5092f51f0cc6/track.jpg"}}},{"index":13,"time":1529400,"duration":238200,"relevance":-1,"hit":false,"text":"TU German Local Hospital Abbreviations example QHCI cm I HWI I Hamwegsmfekt I Hmterwandmfarkt I Hmterwandlschamle I Haxenwurmmfektion I HaIswnrbelimmoblllsation I Hlp WalSt Index I Height Width Index I Heart Work Index I HemodynamlcaIly weighted Imaging I High Water Imake I Hot watei nrlgatlon I Hepatmc weight Index I Haufig wechselndei Intlmpartner I Leltung Nervenleltung Abtellungsleltung Stromleitung Wasserleltung Harnleltung Ableltung 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thddhumm mb mh nl mania nah swam minaret D Wldm lhm d Immq l y Mm mmmmummidmuu nnulhl hlml l 5u nmw1hMIII dul man Ann nun m n Mxmmimum mnmummuumnq mind hunm HON OCIET I cannon nus wuxhnul yudxuck b which m u cumphshmenn m mum in menuved Such yudmclu m ullcd xlandardx They in awed by be m or Iqu Izhon and mount as an escape from anuchy or m molxule mm 5mm thmm In the ullxmuc mom dicum hue mm by me dcmznds n places upm mm summs pnmde oypmmmnex In Kcunl uni augnennuon 0 pm ms and nulpul by mm M he 30 and pure umciuxc mu m pnmdc Foam 1 mm ml be the milk place 0 mm rm lrl on A pause mm muly mums lo 2 xcepuble mndnd olquzlA ny a pevformmce F0 hue muons the 0 1 moderator m1 be found in governmenlll aulhum and vlsdclcpliun mm 1 mm ulxqulzlion 1w mdyudicul mm u m an nubluhed code can beam in focal point of miuum l nf OIJECVIVIS or SVANDAIDIIAHK Sundanis have value wuhm lhtmxlvrx In lhll they help es ubluh quallly Hui mm um accomplish move to sunny um nu men uubumm or ml or qmlny and palm mmce A xlandud lllvw coolduuuon of die bent n producm w um like producls can be pioduced II perm u ihe rpioducixon of mmh mm in mm qumlll and penmlx m consume 0 udp one pmduu or mm mm molhcl by pufonmme in autumn mam ulmlnrham olma Inn and mini ma ptrmlu Ihc mm m on van chum Brown J H U 8 Loweli D J 1972 Standardization and Health Care IEEE Transactions on Biomedical Engineering BME 19 5 331 334 u Gvouu 25 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/278b63ad-6b81-4fff-a724-0ad3597ef070/track.jpg"}}},{"index":20,"time":2131200,"duration":121880,"relevance":-1,"hit":false,"text":"TU Slide 3 2 Still a big problem lnaccuracy of medical data GHQ cm fvariability Io inaccuracy Tar 4 ng a I IECICE hIstor the perforIIIahceo I fa DIWLICE exaIIIIIat on the Int meta IOI labor to e3ts e Ie the CiETIIIIUOII ofI o Iseases re stro ISI IgII inexact Data e IiIed co ected a Id nterpretec With a deg ee of tarIabI ItI ant Inachuac I I I ails f3 short of trIe standard which engineers do expect from most data Moreover standards might be interpreted variably by different medical doctors different hospitals different medical schools different medical cultures Komaroff A L 1979 The variability and inaccuracy of medical data Proceedings of the IEEE 67 9 1196 1207 New Gioua 25 m9 0 19 03","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/684011df-8034-4094-962f-9f48ca1f8831/track.jpg"}}},{"index":21,"time":2253080,"duration":19240,"relevance":-1,"hit":false,"text":"TU Slide 3 3 The patient clinician dialogue from 1979 GHQ cm clinician In uences pauenl s gIving o I DHIUM Patient Influences clinicun s receptIon of infer anon Fluent provides Hmcun callects Information interprets War anon Vulient seeks Chn nan makes tenulive Mic l care decis1ons about diagnosis rein lent and prognos s Patient responds with Chmcun descnbes cerzain feeHngs and explains these decides to lake dens ons to patient those snerweutic anmns recmnded by human Pauem s response influences chn clan s denslons Clmlcun s descripuon exphnatlon in uences pauenl s response Komaroff 1979 Hohmglv Gvoun 27 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/274b376f-b6ca-4409-99ef-644ae835fd37/track.jpg"}}},{"index":22,"time":2272320,"duration":135440,"relevance":-1,"hit":false,"text":"TU Slide 3 4 Standardized data QHCI cm m r mat we sme Um 4 u It uLTU P t 7 O CH 19 65W 5 TC The most used standardized data set to date IS the International Classification of Diseases ICD which was first adopted in 1900 for collecting statistics Ahmadian et al 2011 Holzmglv Group 23 me 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Example ECG xv menu w 25 1 u no my a W up 7 so 0 was n mo r","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/7a401a14-d087-4c9d-9aa2-585cd606d375/track.jpg"}}},{"index":26,"time":2449080,"duration":27200,"relevance":-1,"hit":false,"text":"TU Example ECG a 1 1 Electrocardiography lnle venhon 1_2Vs 4 ECG o a hean In normal Slnus rhythm ICD Q CM 59 52 M 5H I I I Medll nePIUS xv mom nus 4 25 1 LA no v a 10 up 7 so 0 Mac n mo","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/678e080a-f48d-4d87-a1a4-54d291d72b3b/track.jpg"}}},{"index":27,"time":2476280,"duration":31560,"relevance":-1,"hit":false,"text":"QHCI TU Example of the usefulness of ApEn 1 3 cm Holzinger At Stacker Ct Bruschi M Auinger At Silva H Gamboa H Fred A 2012 On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data In Huang R Ghorbani At P051 6 Yamaguchi T Yen N Jin B eds Active Media Technology Lecture Notes in Computer Science LNCS 7669 Berlin Heidelberg Springer pp 646 657 EU Proiect EMERGE l2007 2010l 709 0 1903 HolzmgchvouD 30","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/a651be86-ae60-4783-b905-57e9cf20ee86/track.jpg"}}},{"index":28,"time":2507840,"duration":13680,"relevance":-1,"hit":false,"text":"TU Slide 3 6 Example Annotated ECG signal in HL7 Standard 9H0 cm 2 x http wnkihl7 0rg images S SZ Ecgwg Holzmgu Gvouu n 709 0 19 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pavxmq u r rg m an 9 numu n vuwnwcwamww Allon o m Dams mm mum Eoct v clov sung 1 v V Nuwm m map comm 0 pm n K umECGa Mum Aloas 0 mm w mum New m m A an vac mogul m nu mlabovatm among xvc l Rahonale Vol 3 slandavd HHKI electronic ECG storage Vovmal V0 Huh Ht um to came an 04 mh umrmn 9w wuss v u mumm u Main 1 In mmum atoms plho vl u Nil In 5 men sums 010 0 Sky rg cl ECG mm a rm mu h v mu Hohmgu Gvouv 31 w I o 319 E nag w 4 u ntevx no mc Huwpu uhonand cm mom o duonox a tux om nrvorum EC 0 B ngs w m m 09 g m ECG m mu I P o malro l 5 st Rm rs in ma w w u an M y n may once ECG can no ammo in we v Not an wmvsor so ralm 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/5ddfedfd-24ff-4dc9-aa2a-eba4be95a59e/track.jpg"}}},{"index":31,"time":2578640,"duration":8960,"relevance":-1,"hit":false,"text":"EU Slide 3 8 Standardization of ECG data 1 2 QHCF I There has been a large number of ECG storage formats proclaiming to promote interoperability I There are three predominant ECG formats I SCP ECG 1993 European Standard Binary data I DICOM ECG 2000 European Standard Binary data I HL7 aECG 2001 ANSI Standard XML data I A mass of researchers have been proposing their own ECG storage formats to be considered for implementation proprietary formats I Binary has been the predominant method for storing ECG data Bond R R Finlay D D Nugent C Di Moore 6 2011 A revnew of ECG storage formats International Journal of Medical Informatics 80 10 681 597 New Group 33 709 0 90","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6ce3004f-0df1-4115-a131-27a012ae401c/track.jpg"}}},{"index":32,"time":2587600,"duration":5560,"relevance":-1,"hit":false,"text":"TU Slide 3 9 Standardization of ECG 2 2 QHCI cm Overview on current ECG storage formats Bond R R Finlay D D Nugent C Di Moore 6 2011 A revnew of ECG storage formats International Journal of Medical Informatics 80 10 681 697 Hoizinglvaouv 34 709 0 90","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/a08f32fe-9f62-483c-992c-9a6359fbb799/track.jpg"}}},{"index":33,"time":2593160,"duration":37720,"relevance":-1,"hit":false,"text":"TU Slide 3 9 Standardization of ECG 2 2 QHCI cm Overview on current ECG storage formats nu m an in xx BINARY m 2 2 xx nu rxzz nu w r H mm hINART a 9mm w nu p mm mu nu rm rrxxx nu m w m ms nu Bond R R Finlay D D Nugent C Di Moore 6 2011 A revnew of ECG storage formats InternanonalJaurnal of Medical Informatics 80 10 681 697 Making v Grove 34 me was","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/8a7ec885-b8e7-4653-bb51-6bc7aec6b2da/track.jpg"}}},{"index":34,"time":2630880,"duration":12480,"relevance":-1,"hit":false,"text":"IBSlide 3 10 Example of a Binary ECG file 1 Ln Hchmgtv Gvouv 20 o u n J 30 39 31 3 30 30 30 30 06 04 0 45 6b be 6 60 10 50 9D 10 4C 1 30 SD 48 DE 4 4K 08 U S 44 41 UH m 11 4 S3 01 CH 4d 13 10 4 3 20 33 10 44 S3 20 Bond ex al 2011 35 QHCI l UI 1 2 33 5 0 1 3447l 3 44 o 300 111109100 0 1 IA 2003 11 I I A L002 11 2 VT 2002 1122091E00 0 TH 091000 3 TH 091000 7 SH CS ECG Unknown FN p Lo 0 PH I M laSBOSus 8 5 H AS 016 709 0 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Representation","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/f6a312f1-093d-4cf3-8a4a-e32114b18318/track.jpg"}}},{"index":37,"time":2705200,"duration":258000,"relevance":-1,"hit":false,"text":"TU Examples for famous knowledge representations cm Mallmmaliml lngin mlullr Dr I m x lthIL l mgr l mnn mulul l ml lnmh 1mm l 1 l ullmm KUhIthll I ngk I uul m l wdmlugy liinlngy hum llulxh l nl lL lmmvr Rmuuhlall hllur hh mell lrllun Hnun lL ullm ll l llh u u Km mum llcuhcl 1 an mum liuniml lali it I Am r IlL mnullu Han x l thL Rah m nm u auml Xcluorkx QHCI l umomiu llrnl ham I ll run l nulnmn You runmnn Illll 1mm Rational Again Daws R Shrobe Hr Szolovns P 1993 What IS a knowledge represenlanun7 AlMagazIne 14 1 17 33 mum v Gmuu 0 is we 0 19m","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/9e6a45ca-8129-4d93-ab2f-5dd1ccae2dea/track.jpg"}}},{"index":38,"time":2963200,"duration":157200,"relevance":-1,"hit":false,"text":"IU Logical Representation as a basis for logical reasoning Yes of course That us exactly why I hate you","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/7b0313fd-b2a3-4c6a-8ae1-37c93f83c98f/track.jpg"}}},{"index":39,"time":3120400,"duration":92080,"relevance":-1,"hit":false,"text":"TU Slide 3 24 Formalization versus Expressivity QHCP cm Generm Iogvc Founa onlolog es Modm ogwc Z erstrmder ogm g Duscrwpmn logic 9 Proposmonm logic 9 Forum languages LU Frames Blobel 8 Forum taxonomies 2011 Ontolo bl gy Mela data and D m m S driven healxh XML Schema information dam 70d0 S Database schemes Stems Prmtzxpkzd miormzmunm hmmrchms architeaures XML DTD Thesvun 7nd enable pHealth Struclmed gmsszmes for empowered Thesaun taxonomies patients V Data 1ICIIOHHHCS ms national A 1 I _ _ _ Journalof m WM 5 Glossaries and dam ordinary OSSEHICS Medical V Terms d ctlonanes Informatics 80 g 2 e17 e25 Formallzatlon New Gvoup 40 709 0 90","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/d54e869d-4648-4f76-9180-b3e191e1612f/track.jpg"}}},{"index":40,"time":3212480,"duration":33160,"relevance":-1,"hit":false,"text":"TU Slide 3 12 Example for Modeling of biomedical knowledge QHCP cm Mun Arl Ml cm Ompul Nuan 5mm Ann ole mm Rm Volumo Rod u vmoouly Paulympalhohc Syrup Mlle End Loo I EK VIH C 1 rd Arlway Adwvry Dllllol Molnboll Vommnlndov Hlooe can I Volume Control Ccnlvul Cum c Sud on Wnoul Symp lholz Vllopullm Ind w u _ V1 Rmm Erna Ad my na Anqnohmun u I Envmphm Dlom 5mm Mum WIumn Newman Acum Actuary vnopvusm mm ugmuw I mouuom nyuom pum bulk ow bomnn mum am nvomm I comunmonn Hajduknewncz J R Vicenle K J Doyle D L Mllgram P Burns C M 2001 Modeling a medical envnronmenr an onKology for Integrated medical Informatics desngn InternaImQal Journal of Medical Informatics 62 1 79 99 snmw lcv Baum Halzmg l Gvoun u 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/57c16861-3c39-4165-a0eb-d72493f8dcba/track.jpg"}}},{"index":41,"time":3245640,"duration":42360,"relevance":-1,"hit":false,"text":"TU Slide 3 13 Creating a work domain model WDM 9H0 cm HaJdukieWIcz J R Vicente K 1 Doyle D J Mllgram P Burns g C M 2001 Modeling a medical environment an ontology for Integrated medlcal informatics desngn International Journal of Medical Informatics 52 1 79 99 me 5495 42 Hohmgnv 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Mon hum mum omuuun urn ma Erratum 3mm nu ma Syn m hhneu nu organ B unzu am 1 5mm Inn cmmamm um Balances Emu IA c I a my mm m saw mm m Snowy wh m 9 mug s mm m on inatm w m snug omn m smug om m Sloug oma a hymn um um _ cu van a Bed Kha twp GUM T 75w Warw M Olmnmn Processes 5 va 0 I A 9 W 7 own bu launch m an 5 c3 V 1 Cm w sharia larva th Wl n Physuology Anatomy Hajduklewicz et al 2001 Level of Abstraction Blhncu mm mm s Ebelvomn pN 0 co","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/61d45564-1237-44b5-8d35-25ffee04c233/track.jpg"}}},{"index":44,"time":3338200,"duration":15320,"relevance":-1,"hit":false,"text":"TU Slide 3 16 WDM of b the cardiovascular system cm b L Antaull C wblnn Purposes m Bloc vaun Clrdmmr SWIM Balances un wb am and m Crc blah Vol m hm Pfecesses H5149 l gsnl P Y5 9Y 533 3 1 Anaton HadeKIEWiCZ e 31 2001 Level of Abstraction Ho u Gvouu Pulmmlry ma 5mm 5mm aura um Fm um Imo sum Qum m1 hymn vmwrv no SM Cram on mu m Fo Reinum Ina VmurI Fm Sum ml 5m mummy m 5mm Symon Flu emu 45 Crow vucuu Nut om Bunc um no In W W Duma I Id Irvin Como W o w C wmm Prawn 7a W Fund Sum and SM non na vucuw Newark C mn Funmn mm Rn Rhyflwx emu Anglony QHCI Component vncum ow mm ammo Inn b n mu um snow own ma 1min Dam mu 000 vucum Cwmmwvmv rm Rum nag vnouw Blood vow r m snow and Sm mm m vnnuvcw rungmn nun I Ammo r Cum u vmm vuon rum on AIIII vu mnu nu vncum Alum 709 04953","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/cd45669c-416f-4663-b0c1-b7d5568b6dfd/track.jpg"}}},{"index":45,"time":3353520,"duration":42080,"relevance":-1,"hit":false,"text":"IU_ Slide 3 17 Example Mapping 0R sensors onto the WDM QHCI Level of Aggregation Divergenl Mappmg OneJoom Mappmg Level of Abstraction No Ma mu 3 Convolqenl Mappmq Sensor HBJdUkIEWiCZ et al 2001 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1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/d97a0b4a-e34e-42ab-a2a7-2aafad01c892/track.jpg"}}},{"index":52,"time":3571200,"duration":36720,"relevance":-1,"hit":false,"text":"","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/f424edc1-bed1-4584-9050-5554fa76145b/track.jpg"}}},{"index":53,"time":3607920,"duration":70360,"relevance":-1,"hit":false,"text":"TU Slide 3 20 The first Ontology of what exists QHCP cm marema n wate a ammale danxmale L v Ig Meral senslda 58 511 Simonel M MessaI R Duallo G Ammal plan Simonet A7 2009 Ontologles m he Hea th erId In Berka P h P1 _ F Raucm Z ghed D A Eds a O a a Data Mmmg and Medical Knowledge Management Cases H U U a 3 B ea 5 andApphcanons New York A Medtcallnformanon SCIence 3301185 D a 3 A S O e 93 Reference 3 7 56 mum v Gvoup so 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/c663af23-ed4b-4cec-8a4f-b7230adec5c3/track.jpg"}}},{"index":54,"time":3678280,"duration":24680,"relevance":-1,"hit":false,"text":"TU Slide 3 21 Ontology Classic definition QHCF cm Aristotle attempted to classify the things in the world where it is employed to describe the existence of beings in the world Artificial Intelligence and Knowledge Engineering deals also With reasoning about models of the world Therefore AI researchers adopted the term ontology to describe what can be computationally represented of the world within a program An ontology is a formal explicit specification of a shared conceptualization A conceptualization refers to an abstract model of some phenomenon in the world by havmg identified the relevant concepts of that phenomenon Studer R Benjamins V R Fensel D 1998 Knowledge Engineering Prinmples and methods Data Knowledge Engineering 25 1 2 151 197 Holzingov slow 5 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6cf97c2d-5378-4d6e-93f8-ed8e5e610fae/track.jpg"}}},{"index":55,"time":3702960,"duration":49960,"relevance":-1,"hit":false,"text":"cm TU Example Network Extracted Ontology of the human cell Cam convene Root m Pinion complex r mp2 pvolm comma Raquel on ol anhon Dmnmlmm 4 CW Whom Wk 9 Comcal wan M Cell Ital duh busynmdlc 9 0ch 4 blosy m ptocoss 9 www kurzwewlaw net_lmages_ce l model Eng Credn UC San Diego School of Medlcme gm Gvouc 52 709 0 19 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relations r etlect tne domain semantics For malizes the terminology in the domain Terminology terms definition and usage in the specific context Holzin lv Grove 54 m9 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/e9753eaa-c94a-4991-b67e-aedcfd19c840/track.jpg"}}},{"index":58,"time":3868280,"duration":27520,"relevance":-1,"hit":false,"text":"TU Slide 3 25 Example of a conceptual structure from CogSci GHQ cm Contepl V VS AReLmom _ 9m1 OlReluuomh 3 _ Simonet M Messai R Duallo G Simonet A 2009 Ontologles In the Health Field In Berka P Rauch J Zlghed D A Eds Data Mining and Medical Knowledge Management Cases and Applications New York Medical In ormarian Science Refo ence 37 56 new Gvouv 52 709 0 19 us","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/a73b5113-9370-4da9-96fe-067e3e89bba1/track.jpg"}}},{"index":59,"time":3895800,"duration":105600,"relevance":-1,"hit":false,"text":"IU Slide 3 26 Examples of Biomedical Ontologies 9H0 a M WWW 5 mm um um Mm M Avg W mama 32 3 mt 1 3 w e 7 A L t re ux w 9 Mr 3 r 1 Vic 31 VI e 3 e 3 W 35 06 4 Jute r 33 Bodenrelder O 2008 Biomedical ontologles In action role In knowledge management data Integration and decisnon support Methods of In ormatian In Medicine 47 Supplement 1 67 79 Holzmglv Stone 57 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/fc80b7ed-6136-48db-8ebb-84938af0ca60/track.jpg"}}},{"index":60,"time":4001400,"duration":241640,"relevance":-1,"hit":false,"text":"EU Slide 3 27 Taxonomy of Ontology Languages 9H0 I 1 Graph notations I Semantic networks I Topic Maps ISO IEC 13250 I Unified Modeling Language UML I Resource Description Framework RDF I 2 Logic based I Description Logics e g OIL DAML O L OWL I Rules e g RuIeML LP Prolog I First Order Logic KIF Knowledge Interchange Format I Conceptual graphs I Syntactically higher order logics e g LBase I Non classical logics e g Flogic Non Mon modalities I 3 Probabilistic fuzzy Making Group 53 709 0 19 ma","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/72e430d4-2826-4757-b219-00cb1b9435d1/track.jpg"}}},{"index":61,"time":4243040,"duration":16080,"relevance":-1,"hit":false,"text":"EU Slide 3 27 Taxonomy of Ontology Languages QHCL I 1 Graph notations I Semantic networks I Topic Maps ISO IEC 13250 I Unified Modeling Language UML I Resource Description Framework RDF I 2 Logic based I Description Logics e g OIL DAML O L OWL I Rules e g RuIeML LP Prolog I First Order Logic KIF Knowledge Interchange Format I Conceptual graphs I Syntactically higher order logics e g LBase I Non classical logics e g Flogic Non Mon modalities I 3 Probabilistic fuzzy Holzmglv Grouv 53 m9 and","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/1533341d-07e8-4dca-9e79-c8d737d2957f/track.jpg"}}},{"index":62,"time":4259120,"duration":11160,"relevance":-1,"hit":false,"text":"TU Slide 3 28 Example for 1 Graphical Notation RDF GHQ a onmog ngamsm is A hbkv W as m w Descnphon mm vmumm We Em up 50mm Cheung K Hi Samwald r _ two mesion Mi Auerbach R K SW 5mm um W2 Gersteln M B 2010 Mama won mm Struciured digital tables on me the Semantic Web ioward Scum a structured digital SLVSJ S B onm hmcrwc literature Molecular Systems Biology 6 403 Making Gvoun 59 mg 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/ef7c8359-b2e2-467e-9c64-834137c02949/track.jpg"}}},{"index":63,"time":4270280,"duration":37120,"relevance":-1,"hit":false,"text":"TU Slide 3 29 Example for 2 Web Ontology Language OWL QHCF cm Axiom Example Sub class Alga I Plant Organism Equivalent lass Cv Cancer Neoplastic Protess isjoint with C L Vertebrate nvertebrate Same individual x1 x_ Blueb hark Prionace lauca Different from x x Sea Horse Horse Sub property P I P lias_mother I has_parent Equivalent property P I treated_l y cured_l y Inverse P P location of has ocation Transitive property I P part_of part_of Functional property I lP I lhas_tri utary Inverse functional property II lhas_ cienti e name Bhatt M Rahayu W Soni S P 8 Wouters C 2009 Ontology driven semantic profiling and retrieval In medical information systems Web Semantics Science Services and Agents on the World Wide Web 7 4 317 331 Making Grove so 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/ae9e3143-5bbf-47dc-a011-a2d9810289c6/track.jpg"}}},{"index":64,"time":4307400,"duration":20680,"relevance":-1,"hit":false,"text":"TU Helpful Handbook for Spoken Mathematics QHCL cm Handbook lot web efzglhr dok MAT vkojIc Larrvs_spea keasy pdf 3mm Malhemclm Alan sum HELPFUL https en wikipedIa org WIkI Lls _of_mathematical_symbo s LaTeX Symbols http www artofprob emso vnng com wikI Index php LaTeX Symbols Math ML http wwwmblnlionheart com stds htmId entities mathml The MathML Association promotes funds MathML implementations MathML is an ISO IEC International Standard Holzmgcv Gvoup 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/e5bf195d-2b91-4eb5-a14d-dcc0ce906cd4/track.jpg"}}},{"index":65,"time":4328080,"duration":27920,"relevance":-1,"hit":false,"text":"TU Slide 3 30 OWL class constructors QHCI cm Constructor DL syntax xainple Intersection C t C AnatomicaIAlmormality PathologicaLFunction Union C i i _ C Botly_Sul stance V OrganiLChemical Complement C lnverteln ate One of x x Oestrogen Progesterone All values from VP C Vco occurs_with l lant 3c0_0ccurs_with Animal has_ingretlient ngreclient Some values 3R Max cardinality Min cardinality Bhatt et al 2009 Holzingtt Group 52 709 0 19 03","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/77eedbb1-b129-43d9-b25a-0fe85bdce5e9/track.jpg"}}},{"index":66,"time":4356000,"duration":48000,"relevance":-1,"hit":false,"text":"05 Medical Classifications","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6fc5eef7-82fa-4e58-86f0-a6a312cd3fa2/track.jpg"}}},{"index":67,"time":4404000,"duration":61880,"relevance":-1,"hit":false,"text":"15 H QHCI om ma pm MEN IOU am f l CHI f FICI RI I can H uou wm l l rl Ul 4 l Ill uuxlx ma __ _2 IA vmrw 5 H m w wl IIAAVn m VII llVlNl m I w vmnrm I Ix LL BVHLH m x I 1 run g x INAU m 7 II TWINIYOITII II I 1 In yon mum a nan m NONI n w hm V anon m warm I r Wm IVI gum III ___ XVHVAIL I In XVIHNN um lw m m f _ __ I H luau 1 IM SPECIES f xTLA qARUM r Im __ x unuu ouutn GENERA 1 1I 1 IE New Gvoup 2 4 m9 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/4c0c1774-d649-47c4-b8a1-b7a7dd31ad25/track.jpg"}}},{"index":68,"time":4465880,"duration":108720,"relevance":-1,"hit":false,"text":"TU Slide 3 31 Medical Classifications rough overview QHCF cm 1 Since the classification by Cari VOii Liime 1735 approx 100 various Classi cations in use I itemat one C as i cat on of DiSease iiCDi S 3teiiiati ed Nori eii atm e 0 MediCi iE SWOMEDi Med Cai Skioieit Heaci iga ixieSHi FOL catiO iai Mocei O Aiiatomy iFixiAi Ge ie Oi toiogy iGOi Unified Medical Language System UMLS Hoizinglv Gvouv 2 5 me 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QHCI 1 M l u u I I Lf 1 1 Nomngmcvoup m mg 0 19 a","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/9fd2ba21-1e23-4be2-b944-328f489f4741/track.jpg"}}},{"index":4,"time":988720,"duration":112760,"relevance":-1,"hit":false,"text":"EU Question Why is it so important to know such structures QHCI Winterhaller Cr Widera Pr Krasnogor N 2014 JEPETTOV a Cy oscape plugm for gene se ennchment and lopoluglcal o anaIVSIs based on umeractlon networks Blomiormatlcs 30 I7J 1029 1030 dOI 101093 blomf ormatlcs bn732 mmngu Gvouu u 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6130dd29-9fa8-48b4-9a46-3942fa4e3af8/track.jpg"}}},{"index":5,"time":1101480,"duration":41080,"relevance":-1,"hit":false,"text":"FOR FAIR SELECTION EVERYBODY HAS TO TAKE THE SAME 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report is the most imgortant medium 9H0 cm Radiologischer Befund Kurnnammw SI 9 SNV Fugusl llung Unlenuchung Thorax eme Ebene Ivegend Special Words SB BewegmgsaneVakle Zusland nach Schacelmmxrauma La ng u a ge M ix U Co m um uvurxemmuu keme dkule Slauunguemmn Fragllches Inllll al pmahular h m UF RW Evgass h A b b rey I atIO n S Zustana at Amaoe emes ET me Sp IZO ca 5cm clamaw dev Bv umanon my sonhoxop posmomcn ZVK ubov rL dIL SpwlzL m Flu am GIL VCS KLIH Hmevs um Prmmnomo a Dene Rezessushe Mu koHog mcn Gruf scn Holzinger A Generhofer R Erra h M 2007 Semannsche Informationsextraktlon In medlzlmschen Informatlonssystemen Informank Spektrum 30 2 59 78 mum Gvoun 17 m9 0 1953","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/27284237-fb75-4340-a31f-74ecf81814bd/track.jpg"}}},{"index":10,"time":1357840,"duration":17680,"relevance":-1,"hit":false,"text":"TU Example Chess Game versus Natural Language GHQ cm http stanford edu mplech cszzl apps deepslue html Hokmg v amp 5 mg 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/77300f41-333b-4d87-b72a-6096bdcedf12/track.jpg"}}},{"index":11,"time":1375520,"duration":15800,"relevance":-1,"hit":false,"text":"TU Example Chess Game versus Natural Language QHCF cm http stanford edu mplech cszzl apps deepslue html Hokmgnv Gvouv 3 709 04903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/0cbc64e6-8e73-4db6-9d59-8bc60c88046a/track.jpg"}}},{"index":12,"time":1391320,"duration":138080,"relevance":-1,"hit":false,"text":"TU German Example Synonymity and Ambiguity 9HCI cm Lhil fiwwme e n omum an ux 1 p a LA 1 MM 14 9 1 1L 2 w L__A LL 7 U r L JV L gal P1 7 w a Mr I v 2 A Lu 1 1 L J R7 1 iv 5 1 1 L l 1 M 7 xc J 1 5 1 x 0 Dag mo mnmnmmn qu w w die Antrumschleimhaut ist durch Lymphozyten infiltriert ymphozyt re Infiltration der Antrummukosa Lymphoyteninfiltration der Magenschleimhaut im Antrumbereich Hekmgtv Gvoup 9 me 0490","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/e96ce777-7079-4d1c-839f-5092f51f0cc6/track.jpg"}}},{"index":13,"time":1529400,"duration":238200,"relevance":-1,"hit":false,"text":"TU German Local Hospital Abbreviations example QHCI cm I HWI I Hamwegsmfekt I Hmterwandmfarkt I Hmterwandlschamle I Haxenwurmmfektion I HaIswnrbelimmoblllsation I Hlp WalSt Index I Height Width Index I Heart Work Index I HemodynamlcaIly weighted Imaging I High Water Imake I Hot watei nrlgatlon I Hepatmc weight Index I Haufig wechselndei Intlmpartner I Leltung Nervenleltung Abtellungsleltung Stromleitung Wasserleltung Harnleltung Ableltung 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thddhumm mb mh nl mania nah swam minaret D Wldm lhm d Immq l y Mm mmmmummidmuu nnulhl hlml l 5u nmw1hMIII dul man Ann nun m n Mxmmimum mnmummuumnq mind hunm HON OCIET I cannon nus wuxhnul yudxuck b which m u cumphshmenn m mum in menuved Such yudmclu m ullcd xlandardx They in awed by be m or Iqu Izhon and mount as an escape from anuchy or m molxule mm 5mm thmm In the ullxmuc mom dicum hue mm by me dcmznds n places upm mm summs pnmde oypmmmnex In Kcunl uni augnennuon 0 pm ms and nulpul by mm M he 30 and pure umciuxc mu m pnmdc Foam 1 mm ml be the milk place 0 mm rm lrl on A pause mm muly mums lo 2 xcepuble mndnd olquzlA ny a pevformmce F0 hue muons the 0 1 moderator m1 be found in governmenlll aulhum and vlsdclcpliun mm 1 mm ulxqulzlion 1w mdyudicul mm u m an nubluhed code can beam in focal point of miuum l nf OIJECVIVIS or SVANDAIDIIAHK Sundanis have value wuhm lhtmxlvrx In lhll they help es ubluh quallly Hui mm um accomplish move to sunny um nu men uubumm or ml or qmlny and palm mmce A xlandud lllvw coolduuuon of die bent n producm w um like producls can be pioduced II perm u ihe rpioducixon of mmh mm in mm qumlll and penmlx m consume 0 udp one pmduu or mm mm molhcl by pufonmme in autumn mam ulmlnrham olma Inn and mini ma ptrmlu Ihc mm m on van chum Brown J H U 8 Loweli D J 1972 Standardization and Health Care IEEE Transactions on Biomedical Engineering BME 19 5 331 334 u Gvouu 25 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/278b63ad-6b81-4fff-a724-0ad3597ef070/track.jpg"}}},{"index":20,"time":2131200,"duration":121880,"relevance":-1,"hit":false,"text":"TU Slide 3 2 Still a big problem lnaccuracy of medical data GHQ cm fvariability Io inaccuracy Tar 4 ng a I IECICE hIstor the perforIIIahceo I fa DIWLICE exaIIIIIat on the Int meta IOI labor to e3ts e Ie the CiETIIIIUOII ofI o Iseases re stro ISI IgII inexact Data e IiIed co ected a Id nterpretec With a deg ee of tarIabI ItI ant Inachuac I I I ails f3 short of trIe standard which engineers do expect from most data Moreover standards might be interpreted variably by different medical doctors different hospitals different medical schools different medical cultures Komaroff A L 1979 The variability and inaccuracy of medical data Proceedings of the IEEE 67 9 1196 1207 New Gioua 25 m9 0 19 03","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/684011df-8034-4094-962f-9f48ca1f8831/track.jpg"}}},{"index":21,"time":2253080,"duration":19240,"relevance":-1,"hit":false,"text":"TU Slide 3 3 The patient clinician dialogue from 1979 GHQ cm clinician In uences pauenl s gIving o I DHIUM Patient Influences clinicun s receptIon of infer anon Fluent provides Hmcun callects Information interprets War anon Vulient seeks Chn nan makes tenulive Mic l care decis1ons about diagnosis rein lent and prognos s Patient responds with Chmcun descnbes cerzain feeHngs and explains these decides to lake dens ons to patient those snerweutic anmns recmnded by human Pauem s response influences chn clan s denslons Clmlcun s descripuon exphnatlon in uences pauenl s response Komaroff 1979 Hohmglv Gvoun 27 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/274b376f-b6ca-4409-99ef-644ae835fd37/track.jpg"}}},{"index":22,"time":2272320,"duration":135440,"relevance":-1,"hit":false,"text":"TU Slide 3 4 Standardized data QHCI cm m r mat we sme Um 4 u It uLTU P t 7 O CH 19 65W 5 TC The most used standardized data set to date IS the International Classification of Diseases ICD which was first adopted in 1900 for collecting statistics Ahmadian et al 2011 Holzmglv Group 23 me 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Example ECG xv menu w 25 1 u no my a W up 7 so 0 was n mo r","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/7a401a14-d087-4c9d-9aa2-585cd606d375/track.jpg"}}},{"index":26,"time":2449080,"duration":27200,"relevance":-1,"hit":false,"text":"TU Example ECG a 1 1 Electrocardiography lnle venhon 1_2Vs 4 ECG o a hean In normal Slnus rhythm ICD Q CM 59 52 M 5H I I I Medll nePIUS xv mom nus 4 25 1 LA no v a 10 up 7 so 0 Mac n mo","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/678e080a-f48d-4d87-a1a4-54d291d72b3b/track.jpg"}}},{"index":27,"time":2476280,"duration":31560,"relevance":-1,"hit":false,"text":"QHCI TU Example of the usefulness of ApEn 1 3 cm Holzinger At Stacker Ct Bruschi M Auinger At Silva H Gamboa H Fred A 2012 On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data In Huang R Ghorbani At P051 6 Yamaguchi T Yen N Jin B eds Active Media Technology Lecture Notes in Computer Science LNCS 7669 Berlin Heidelberg Springer pp 646 657 EU Proiect EMERGE l2007 2010l 709 0 1903 HolzmgchvouD 30","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/a651be86-ae60-4783-b905-57e9cf20ee86/track.jpg"}}},{"index":28,"time":2507840,"duration":13680,"relevance":-1,"hit":false,"text":"TU Slide 3 6 Example Annotated ECG signal in HL7 Standard 9H0 cm 2 x http wnkihl7 0rg images S SZ Ecgwg Holzmgu Gvouu n 709 0 19 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pavxmq u r rg m an 9 numu n vuwnwcwamww Allon o m Dams mm mum Eoct v clov sung 1 v V Nuwm m map comm 0 pm n K umECGa Mum Aloas 0 mm w mum New m m A an vac mogul m nu mlabovatm among xvc l Rahonale Vol 3 slandavd HHKI electronic ECG storage Vovmal V0 Huh Ht um to came an 04 mh umrmn 9w wuss v u mumm u Main 1 In mmum atoms plho vl u Nil In 5 men sums 010 0 Sky rg cl ECG mm a rm mu h v mu Hohmgu Gvouv 31 w I o 319 E nag w 4 u ntevx no mc Huwpu uhonand cm mom o duonox a tux om nrvorum EC 0 B ngs w m m 09 g m ECG m mu I P o malro l 5 st Rm rs in ma w w u an M y n may once ECG can no ammo in we v Not an wmvsor so ralm 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/5ddfedfd-24ff-4dc9-aa2a-eba4be95a59e/track.jpg"}}},{"index":31,"time":2578640,"duration":8960,"relevance":-1,"hit":false,"text":"EU Slide 3 8 Standardization of ECG data 1 2 QHCF I There has been a large number of ECG storage formats proclaiming to promote interoperability I There are three predominant ECG formats I SCP ECG 1993 European Standard Binary data I DICOM ECG 2000 European Standard Binary data I HL7 aECG 2001 ANSI Standard XML data I A mass of researchers have been proposing their own ECG storage formats to be considered for implementation proprietary formats I Binary has been the predominant method for storing ECG data Bond R R Finlay D D Nugent C Di Moore 6 2011 A revnew of ECG storage formats International Journal of Medical Informatics 80 10 681 597 New Group 33 709 0 90","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6ce3004f-0df1-4115-a131-27a012ae401c/track.jpg"}}},{"index":32,"time":2587600,"duration":5560,"relevance":-1,"hit":false,"text":"TU Slide 3 9 Standardization of ECG 2 2 QHCI cm Overview on current ECG storage formats Bond R R Finlay D D Nugent C Di Moore 6 2011 A revnew of ECG storage formats International Journal of Medical Informatics 80 10 681 697 Hoizinglvaouv 34 709 0 90","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/a08f32fe-9f62-483c-992c-9a6359fbb799/track.jpg"}}},{"index":33,"time":2593160,"duration":37720,"relevance":-1,"hit":false,"text":"TU Slide 3 9 Standardization of ECG 2 2 QHCI cm Overview on current ECG storage formats nu m an in xx BINARY m 2 2 xx nu rxzz nu w r H mm hINART a 9mm w nu p mm mu nu rm rrxxx nu m w m ms nu Bond R R Finlay D D Nugent C Di Moore 6 2011 A revnew of ECG storage formats InternanonalJaurnal of Medical Informatics 80 10 681 697 Making v Grove 34 me was","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/8a7ec885-b8e7-4653-bb51-6bc7aec6b2da/track.jpg"}}},{"index":34,"time":2630880,"duration":12480,"relevance":-1,"hit":false,"text":"IBSlide 3 10 Example of a Binary ECG file 1 Ln Hchmgtv Gvouv 20 o u n J 30 39 31 3 30 30 30 30 06 04 0 45 6b be 6 60 10 50 9D 10 4C 1 30 SD 48 DE 4 4K 08 U S 44 41 UH m 11 4 S3 01 CH 4d 13 10 4 3 20 33 10 44 S3 20 Bond ex al 2011 35 QHCI l UI 1 2 33 5 0 1 3447l 3 44 o 300 111109100 0 1 IA 2003 11 I I A L002 11 2 VT 2002 1122091E00 0 TH 091000 3 TH 091000 7 SH CS ECG Unknown FN p Lo 0 PH I M laSBOSus 8 5 H AS 016 709 0 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Representation","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/f6a312f1-093d-4cf3-8a4a-e32114b18318/track.jpg"}}},{"index":37,"time":2705200,"duration":258000,"relevance":-1,"hit":false,"text":"TU Examples for famous knowledge representations cm Mallmmaliml lngin mlullr Dr I m x lthIL l mgr l mnn mulul l ml lnmh 1mm l 1 l ullmm KUhIthll I ngk I uul m l wdmlugy liinlngy hum llulxh l nl lL lmmvr Rmuuhlall hllur hh mell lrllun Hnun lL ullm ll l llh u u Km mum llcuhcl 1 an mum liuniml lali it I Am r IlL mnullu Han x l thL Rah m nm u auml Xcluorkx QHCI l umomiu llrnl ham I ll run l nulnmn You runmnn Illll 1mm Rational Again Daws R Shrobe Hr Szolovns P 1993 What IS a knowledge represenlanun7 AlMagazIne 14 1 17 33 mum v Gmuu 0 is we 0 19m","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/9e6a45ca-8129-4d93-ab2f-5dd1ccae2dea/track.jpg"}}},{"index":38,"time":2963200,"duration":157200,"relevance":-1,"hit":false,"text":"IU Logical Representation as a basis for logical reasoning Yes of course That us exactly why I hate you","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/7b0313fd-b2a3-4c6a-8ae1-37c93f83c98f/track.jpg"}}},{"index":39,"time":3120400,"duration":92080,"relevance":-1,"hit":false,"text":"TU Slide 3 24 Formalization versus Expressivity QHCP cm Generm Iogvc Founa onlolog es Modm ogwc Z erstrmder ogm g Duscrwpmn logic 9 Proposmonm logic 9 Forum languages LU Frames Blobel 8 Forum taxonomies 2011 Ontolo bl gy Mela data and D m m S driven healxh XML Schema information dam 70d0 S Database schemes Stems Prmtzxpkzd miormzmunm hmmrchms architeaures XML DTD Thesvun 7nd enable pHealth Struclmed gmsszmes for empowered Thesaun taxonomies patients V Data 1ICIIOHHHCS ms national A 1 I _ _ _ Journalof m WM 5 Glossaries and dam ordinary OSSEHICS Medical V Terms d ctlonanes Informatics 80 g 2 e17 e25 Formallzatlon New Gvoup 40 709 0 90","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/d54e869d-4648-4f76-9180-b3e191e1612f/track.jpg"}}},{"index":40,"time":3212480,"duration":33160,"relevance":-1,"hit":false,"text":"TU Slide 3 12 Example for Modeling of biomedical knowledge QHCP cm Mun Arl Ml cm Ompul Nuan 5mm Ann ole mm Rm Volumo Rod u vmoouly Paulympalhohc Syrup Mlle End Loo I EK VIH C 1 rd Arlway Adwvry Dllllol Molnboll Vommnlndov Hlooe can I Volume Control Ccnlvul Cum c Sud on Wnoul Symp lholz Vllopullm Ind w u _ V1 Rmm Erna Ad my na Anqnohmun u I Envmphm Dlom 5mm Mum WIumn Newman Acum Actuary vnopvusm mm ugmuw I mouuom nyuom pum bulk ow bomnn mum am nvomm I comunmonn Hajduknewncz J R Vicenle K J Doyle D L Mllgram P Burns C M 2001 Modeling a medical envnronmenr an onKology for Integrated medical Informatics desngn InternaImQal Journal of Medical Informatics 62 1 79 99 snmw lcv Baum Halzmg l Gvoun u 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/57c16861-3c39-4165-a0eb-d72493f8dcba/track.jpg"}}},{"index":41,"time":3245640,"duration":42360,"relevance":-1,"hit":false,"text":"TU Slide 3 13 Creating a work domain model WDM 9H0 cm HaJdukieWIcz J R Vicente K 1 Doyle D J Mllgram P Burns g C M 2001 Modeling a medical environment an ontology for Integrated medlcal informatics desngn International Journal of Medical Informatics 52 1 79 99 me 5495 42 Hohmgnv 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Mon hum mum omuuun urn ma Erratum 3mm nu ma Syn m hhneu nu organ B unzu am 1 5mm Inn cmmamm um Balances Emu IA c I a my mm m saw mm m Snowy wh m 9 mug s mm m on inatm w m snug omn m smug om m Sloug oma a hymn um um _ cu van a Bed Kha twp GUM T 75w Warw M Olmnmn Processes 5 va 0 I A 9 W 7 own bu launch m an 5 c3 V 1 Cm w sharia larva th Wl n Physuology Anatomy Hajduklewicz et al 2001 Level of Abstraction Blhncu mm mm s Ebelvomn pN 0 co","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/61d45564-1237-44b5-8d35-25ffee04c233/track.jpg"}}},{"index":44,"time":3338200,"duration":15320,"relevance":-1,"hit":false,"text":"TU Slide 3 16 WDM of b the cardiovascular system cm b L Antaull C wblnn Purposes m Bloc vaun Clrdmmr SWIM Balances un wb am and m Crc blah Vol m hm Pfecesses H5149 l gsnl P Y5 9Y 533 3 1 Anaton HadeKIEWiCZ e 31 2001 Level of Abstraction Ho u Gvouu Pulmmlry ma 5mm 5mm aura um Fm um Imo sum Qum m1 hymn vmwrv no SM Cram on mu m Fo Reinum Ina VmurI Fm Sum ml 5m mummy m 5mm Symon Flu emu 45 Crow vucuu Nut om Bunc um no In W W Duma I Id Irvin Como W o w C wmm Prawn 7a W Fund Sum and SM non na vucuw Newark C mn Funmn mm Rn Rhyflwx emu Anglony QHCI Component vncum ow mm ammo Inn b n mu um snow own ma 1min Dam mu 000 vucum Cwmmwvmv rm Rum nag vnouw Blood vow r m snow and Sm mm m vnnuvcw rungmn nun I Ammo r Cum u vmm vuon rum on AIIII vu mnu nu vncum Alum 709 04953","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/cd45669c-416f-4663-b0c1-b7d5568b6dfd/track.jpg"}}},{"index":45,"time":3353520,"duration":42080,"relevance":-1,"hit":false,"text":"IU_ Slide 3 17 Example Mapping 0R sensors onto the WDM QHCI Level of Aggregation Divergenl Mappmg OneJoom Mappmg Level of Abstraction No Ma mu 3 Convolqenl Mappmq Sensor HBJdUkIEWiCZ et al 2001 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1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/d97a0b4a-e34e-42ab-a2a7-2aafad01c892/track.jpg"}}},{"index":52,"time":3571200,"duration":36720,"relevance":-1,"hit":false,"text":"","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/f424edc1-bed1-4584-9050-5554fa76145b/track.jpg"}}},{"index":53,"time":3607920,"duration":70360,"relevance":-1,"hit":false,"text":"TU Slide 3 20 The first Ontology of what exists QHCP cm marema n wate a ammale danxmale L v Ig Meral senslda 58 511 Simonel M MessaI R Duallo G Ammal plan Simonet A7 2009 Ontologles m he Hea th erId In Berka P h P1 _ F Raucm Z ghed D A Eds a O a a Data Mmmg and Medical Knowledge Management Cases H U U a 3 B ea 5 andApphcanons New York A Medtcallnformanon SCIence 3301185 D a 3 A S O e 93 Reference 3 7 56 mum v Gvoup so 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/c663af23-ed4b-4cec-8a4f-b7230adec5c3/track.jpg"}}},{"index":54,"time":3678280,"duration":24680,"relevance":-1,"hit":false,"text":"TU Slide 3 21 Ontology Classic definition QHCF cm Aristotle attempted to classify the things in the world where it is employed to describe the existence of beings in the world Artificial Intelligence and Knowledge Engineering deals also With reasoning about models of the world Therefore AI researchers adopted the term ontology to describe what can be computationally represented of the world within a program An ontology is a formal explicit specification of a shared conceptualization A conceptualization refers to an abstract model of some phenomenon in the world by havmg identified the relevant concepts of that phenomenon Studer R Benjamins V R Fensel D 1998 Knowledge Engineering Prinmples and methods Data Knowledge Engineering 25 1 2 151 197 Holzingov slow 5 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6cf97c2d-5378-4d6e-93f8-ed8e5e610fae/track.jpg"}}},{"index":55,"time":3702960,"duration":49960,"relevance":-1,"hit":false,"text":"cm TU Example Network Extracted Ontology of the human cell Cam convene Root m Pinion complex r mp2 pvolm comma Raquel on ol anhon Dmnmlmm 4 CW Whom Wk 9 Comcal wan M Cell Ital duh busynmdlc 9 0ch 4 blosy m ptocoss 9 www kurzwewlaw net_lmages_ce l model Eng Credn UC San Diego School of Medlcme gm Gvouc 52 709 0 19 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relations r etlect tne domain semantics For malizes the terminology in the domain Terminology terms definition and usage in the specific context Holzin lv Grove 54 m9 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/e9753eaa-c94a-4991-b67e-aedcfd19c840/track.jpg"}}},{"index":58,"time":3868280,"duration":27520,"relevance":-1,"hit":false,"text":"TU Slide 3 25 Example of a conceptual structure from CogSci GHQ cm Contepl V VS AReLmom _ 9m1 OlReluuomh 3 _ Simonet M Messai R Duallo G Simonet A 2009 Ontologles In the Health Field In Berka P Rauch J Zlghed D A Eds Data Mining and Medical Knowledge Management Cases and Applications New York Medical In ormarian Science Refo ence 37 56 new Gvouv 52 709 0 19 us","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/a73b5113-9370-4da9-96fe-067e3e89bba1/track.jpg"}}},{"index":59,"time":3895800,"duration":105600,"relevance":-1,"hit":false,"text":"IU Slide 3 26 Examples of Biomedical Ontologies 9H0 a M WWW 5 mm um um Mm M Avg W mama 32 3 mt 1 3 w e 7 A L t re ux w 9 Mr 3 r 1 Vic 31 VI e 3 e 3 W 35 06 4 Jute r 33 Bodenrelder O 2008 Biomedical ontologles In action role In knowledge management data Integration and decisnon support Methods of In ormatian In Medicine 47 Supplement 1 67 79 Holzmglv Stone 57 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/fc80b7ed-6136-48db-8ebb-84938af0ca60/track.jpg"}}},{"index":60,"time":4001400,"duration":241640,"relevance":-1,"hit":false,"text":"EU Slide 3 27 Taxonomy of Ontology Languages 9H0 I 1 Graph notations I Semantic networks I Topic Maps ISO IEC 13250 I Unified Modeling Language UML I Resource Description Framework RDF I 2 Logic based I Description Logics e g OIL DAML O L OWL I Rules e g RuIeML LP Prolog I First Order Logic KIF Knowledge Interchange Format I Conceptual graphs I Syntactically higher order logics e g LBase I Non classical logics e g Flogic Non Mon modalities I 3 Probabilistic fuzzy Making Group 53 709 0 19 ma","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/72e430d4-2826-4757-b219-00cb1b9435d1/track.jpg"}}},{"index":61,"time":4243040,"duration":16080,"relevance":-1,"hit":false,"text":"EU Slide 3 27 Taxonomy of Ontology Languages QHCL I 1 Graph notations I Semantic networks I Topic Maps ISO IEC 13250 I Unified Modeling Language UML I Resource Description Framework RDF I 2 Logic based I Description Logics e g OIL DAML O L OWL I Rules e g RuIeML LP Prolog I First Order Logic KIF Knowledge Interchange Format I Conceptual graphs I Syntactically higher order logics e g LBase I Non classical logics e g Flogic Non Mon modalities I 3 Probabilistic fuzzy Holzmglv Grouv 53 m9 and","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/1533341d-07e8-4dca-9e79-c8d737d2957f/track.jpg"}}},{"index":62,"time":4259120,"duration":11160,"relevance":-1,"hit":false,"text":"TU Slide 3 28 Example for 1 Graphical Notation RDF GHQ a onmog ngamsm is A hbkv W as m w Descnphon mm vmumm We Em up 50mm Cheung K Hi Samwald r _ two mesion Mi Auerbach R K SW 5mm um W2 Gersteln M B 2010 Mama won mm Struciured digital tables on me the Semantic Web ioward Scum a structured digital SLVSJ S B onm hmcrwc literature Molecular Systems Biology 6 403 Making Gvoun 59 mg 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/ef7c8359-b2e2-467e-9c64-834137c02949/track.jpg"}}},{"index":63,"time":4270280,"duration":37120,"relevance":-1,"hit":false,"text":"TU Slide 3 29 Example for 2 Web Ontology Language OWL QHCF cm Axiom Example Sub class Alga I Plant Organism Equivalent lass Cv Cancer Neoplastic Protess isjoint with C L Vertebrate nvertebrate Same individual x1 x_ Blueb hark Prionace lauca Different from x x Sea Horse Horse Sub property P I P lias_mother I has_parent Equivalent property P I treated_l y cured_l y Inverse P P location of has ocation Transitive property I P part_of part_of Functional property I lP I lhas_tri utary Inverse functional property II lhas_ cienti e name Bhatt M Rahayu W Soni S P 8 Wouters C 2009 Ontology driven semantic profiling and retrieval In medical information systems Web Semantics Science Services and Agents on the World Wide Web 7 4 317 331 Making Grove so 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/ae9e3143-5bbf-47dc-a011-a2d9810289c6/track.jpg"}}},{"index":64,"time":4307400,"duration":20680,"relevance":-1,"hit":false,"text":"TU Helpful Handbook for Spoken Mathematics QHCL cm Handbook lot web efzglhr dok MAT vkojIc Larrvs_spea keasy pdf 3mm Malhemclm Alan sum HELPFUL https en wikipedIa org WIkI Lls _of_mathematical_symbo s LaTeX Symbols http www artofprob emso vnng com wikI Index php LaTeX Symbols Math ML http wwwmblnlionheart com stds htmId entities mathml The MathML Association promotes funds MathML implementations MathML is an ISO IEC International Standard Holzmgcv Gvoup 709 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/e5bf195d-2b91-4eb5-a14d-dcc0ce906cd4/track.jpg"}}},{"index":65,"time":4328080,"duration":27920,"relevance":-1,"hit":false,"text":"TU Slide 3 30 OWL class constructors QHCI cm Constructor DL syntax xainple Intersection C t C AnatomicaIAlmormality PathologicaLFunction Union C i i _ C Botly_Sul stance V OrganiLChemical Complement C lnverteln ate One of x x Oestrogen Progesterone All values from VP C Vco occurs_with l lant 3c0_0ccurs_with Animal has_ingretlient ngreclient Some values 3R Max cardinality Min cardinality Bhatt et al 2009 Holzingtt Group 52 709 0 19 03","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/77eedbb1-b129-43d9-b25a-0fe85bdce5e9/track.jpg"}}},{"index":66,"time":4356000,"duration":48000,"relevance":-1,"hit":false,"text":"05 Medical Classifications","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6fc5eef7-82fa-4e58-86f0-a6a312cd3fa2/track.jpg"}}},{"index":67,"time":4404000,"duration":61880,"relevance":-1,"hit":false,"text":"15 H QHCI om ma pm MEN IOU am f l CHI f FICI RI I can H uou wm l l rl Ul 4 l Ill uuxlx ma __ _2 IA vmrw 5 H m w wl IIAAVn m VII llVlNl m I w vmnrm I Ix LL BVHLH m x I 1 run g x INAU m 7 II TWINIYOITII II I 1 In yon mum a nan m NONI n w hm V anon m warm I r Wm IVI gum III ___ XVHVAIL I In XVIHNN um lw m m f _ __ I H luau 1 IM SPECIES f xTLA qARUM r Im __ x unuu ouutn GENERA 1 1I 1 IE New Gvoup 2 4 m9 0 1903","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/4c0c1774-d649-47c4-b8a1-b7a7dd31ad25/track.jpg"}}},{"index":68,"time":4465880,"duration":108720,"relevance":-1,"hit":false,"text":"TU Slide 3 31 Medical Classifications rough overview QHCF cm 1 Since the classification by Cari VOii Liime 1735 approx 100 various Classi cations in use I itemat one C as i cat on of DiSease iiCDi S 3teiiiati ed Nori eii atm e 0 MediCi iE SWOMEDi Med Cai Skioieit Heaci iga ixieSHi FOL catiO iai Mocei O Aiiatomy iFixiAi Ge ie Oi toiogy iGOi Unified Medical Language System UMLS Hoizinglv Gvouv 2 5 me 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Ofdise I ECCI UZS ert HotiL d6 ah A900imer natiowa Statigtica mgttute HEM acteot Bet MCI wet A933 5m Edition A94S HO A965 iCDaS A9 9 iCDiA O 2015 DA we 2018 ICD ll adopt Making Gvouu 57 709 0 190","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/6e5ad176-1e51-4bb4-9b40-6a965176bea4/track.jpg"}}},{"index":71,"time":4683320,"duration":6240,"relevance":-1,"hit":false,"text":"TU Slide 3 33 International Classification of Diseases ICD QHCF cm L629 Lon oriBHsofh iortam 9 Aw W Iliam Farr Lem one f smear of wucnca stat gitcw List 3f Hit cf 1 s uu Hoizingtv 5mg 57 709 0490","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/9c5d84d9-948a-4804-aced-d3591ac1d9bb/track.jpg"}}},{"index":72,"time":4689560,"duration":91960,"relevance":-1,"hit":false,"text":"TU Slide 3 34 Systematized Nomenclature of Medicine SNOMED 9H0 cm 1965 SNOP 1974 SNOMED 1979 SNOMED 1997 Logical Observation Identifiers Names and Codes LOINC integrated into SNOMED 2000 SNOMED RT 2002 SNOMED CT wunmmwm uni n mm mom I f stNnAHUs DhlinvmwvMELANISAHHN u 239 pages SNOMED 6T3 Technical Reference Guide January 2011 International Release US English http wwwisbinhs uk documenIs Isb OOSA amd 26 2006 techrefgund pdf 709 c 1901 Making Gvouu ss","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/980c7eac-2703-4c6f-b3c5-0f494896c341/track.jpg"}}},{"index":73,"time":4781520,"duration":62560,"relevance":-1,"hit":false,"text":"TU Slide 3 35 SNOMED Example Hypertension 9H0 am A 24184005 Finding of increased blood pressure finding 3893600 Abnormal blood pressure finding AND roIeGroup SOME 363714003llnterprets attribute SOME 75367002 B ood pressure observable entity 12763006 Finding of decreased blood pressure finding 392570002 Blood pressure finding finding AND roIeGroup SOME 363714003llnterprets attribute SOME 75367002 Blood pressure observable entity Rector A L Brandt 5 2008 Why Do It the Hard Way The Case for an Expressive Description Logic for SNOMED Journal of the American Medical Informatics Association 15 6 744 751 Holzmglr Grow 2 9 709 0 19 m","previews":{"preview":{"ref":"b69f0c88-071b-45ea-a24f-1e466d9ca665","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/a87e2616-96ee-489a-b8c1-cfe56ad0d8cc/5c773524-2f55-45b5-a2aa-c18c272a8f47/track.jpg"}}},{"index":74,"time":4844080,"duration":10080,"relevance":-1,"hit":false,"text":"IU Slide 3 36 Medical Subject Headings MeSH QHCP MeSH thesaurus is produced by the National Library of Medicine NLM since 1960 Used for cataloging documents and related media and as an index to search these documents in a database and is part of the metathesaurus ofthe Unified Medical Language System UMLS Holzmglv 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