{"id":18999,"date":"2025-11-11T11:20:18","date_gmt":"2025-11-11T11:20:18","guid":{"rendered":"https:\/\/smion.com\/?p=18999"},"modified":"2025-11-11T11:21:03","modified_gmt":"2025-11-11T11:21:03","slug":"predvidanje-zauzetosti-bolnickih-kreveta-kako-neuronske-mreze-pomazu-planiranju-u-bolnicama","status":"publish","type":"post","link":"https:\/\/smion.com\/hr\/predvidanje-zauzetosti-bolnickih-kreveta-kako-neuronske-mreze-pomazu-planiranju-u-bolnicama\/","title":{"rendered":"Predvi\u0111anje zauzetosti bolni\u010dkih kreveta: kako neuronske mre\u017ee poma\u017eu planiranju u bolnicama"},"content":{"rendered":"\t\t
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Kako bolnice mogu predvidjeti broj pacijenata koje \u0107e primiti sutra \u2013 i to s nekoliko redaka koda? Na na\u0161em AI2MED webinaru<\/strong> istra\u017eena je upravo ta tema: primjena neuronskih mre\u017ea u predvi\u0111anju bolni\u010dke zauzetosti i optimizaciji planiranja resursa.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Ciljevi AI2MED projekta<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Projekt AI2MED<\/a> ima za cilj razviti AI kompetencije u zdravstvenom sektoru<\/strong>, kroz edukaciju i suradnju s 16 europskih partnera. Na\u0161 je fokus na razvoju odr\u017eivih AI rje\u0161enja za zdravstvene izazove u Hrvatskoj<\/em> \u2013 ne samo kroz tehnologiju, ve\u0107 i kroz potporu stru\u010dnjacima koji \u0107e ih znati koristiti.<\/p>

Kroz aktivnosti poput webinara i meetupa, AI2MED povezuje istra\u017eiva\u010de, lije\u010dnike i IT stru\u010dnjake te poti\u010de zajednicu koja dijeli znanje i iskustva.<\/p>

Filip Stipan\u010di\u0107<\/strong> upoznao je sve prisutne s AI2MED projektom, a generalni direktor Klinikona Roland Trefftz<\/strong>, keynote govornik na\u0161eg posljednjeg webinara<\/a>, pru\u017eio je uvod u neuralne mre\u017ee kroz pri\u010du o uspjehu u kojoj su voditelji pekare u Njema\u010dkoj iskoristili model neuronskih mre\u017ea kako bi predvidjeli koli\u010dinu kruha potrebnu za sljede\u0107i dan. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Neuralne mre\u017ee i predvi\u0111anje zauzetosti bolni\u010dkih kreveta<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Glavni govornik, Hannes Schmidt<\/strong> iz Klinikon GmbH, predstavio je primjer jednostavnih neuronskih mre\u017ea koje analiziraju vi\u0161egodi\u0161nje podatke o zauzetosti kreveta. Modeli su trenirani na pet godina podataka<\/strong>, uklju\u010duju\u0107i dnevne i no\u0107ne brojke pacijenata, a u nekim su odjelima postigli pogre\u0161ku predvi\u0111anja od samo 3,85%<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Schmidt je pojasnio da su kori\u0161tene jednostavne mre\u017ee s do 120 neurona<\/strong>, trenirane iterativno kako bi se minimizirala pogre\u0161ka.
Zanimljivo, dodavanje vi\u0161e podataka ne mora uvijek zna\u010diti bolju preciznost. \u201ePetnaest no\u0107i je dovoljno u ovom slu\u010daju \u2013 20 ili 30 ne bi zna\u010dajno pove\u0107alo to\u010dnost<\/em>,\u201c napomenuo je Schmidt. Kao primjer, spomenuo je i mogu\u0107nost uklju\u010divanja dijagnoza kao varijabli za predvi\u0111anje duljine boravka pacijenata.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Prikaz Recurrent Neural Networka i pobolj\u0161ane predikcije<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Jednostavna implementacija, \u0161iroka primjena<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Prema Rolandu Trefftzu<\/strong>, jedan od ciljeva projekta je pokazati da AI mo\u017ee biti jednostavan za implementaciju<\/strong>: \u201eDanas za prediktivni model treba vam tek stranica koda i ne\u0161to podataka \u2013 ni\u0161ta vi\u0161e<\/em>,\u201c rekao je.<\/p>

Sustavi koji predvi\u0111aju zauzetost mogu bolnicama pomo\u0107i u planiranju osoblja i operacija<\/strong>, automatizirati unos podataka te se integrirati u alate poput Excela. Cilj nije zamijeniti ljudsku prosudbu, ve\u0107 je poduprijeti alatima koji olak\u0161avaju svakodnevne odluke<\/strong>.<\/p>

Trefftz je naglasio i prakti\u010dnu dimenziju: \u201eSlobodno nam se javite \u2013 rado \u0107emo vam pomo\u0107i da iz va\u0161ih podataka izvu\u010dete model koji mo\u017eete sami koristiti<\/em>.\u201c Takva suradnja omogu\u0107it \u0107e bolnicama da brzo testiraju i prilagode AI modele vlastitim odjelima.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Dovoljna je jedna stranica koda<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Ograni\u010denja i odgovorna uporaba<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Iako rezultati izgledaju obe\u0107avaju\u0107e, stru\u010dnjaci su upozorili na potrebu za stalnim pra\u0107enjem i validacijom modela<\/strong>. \u201eNeuralne mre\u017ee su statistika \u2013 ne mogu predvidjeti nepredvidive doga\u0111aje<\/em>,\u201c rekao je Schmidt, podsje\u0107aju\u0107i da pandemije i drugi ekstremni slu\u010dajevi izlaze izvan domene modela.<\/p>

Hannes Schmidt dodao je da je kriti\u010dko razmi\u0161ljanje klju\u010dno: \u201eOvaj sustav nije izgovor da isklju\u010dite vlastiti mozak. AI mo\u017ee pomo\u0107i, ali odgovornost ostaje na ljudima.\u201c<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Primaj obavijesti o nadolaze\u0107im prilikama<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Prediktivni modeli poput onih prikazanih na webinaru pokazuju koliko AI mo\u017ee unaprijediti u\u010dinkovitost i planiranje u bolnicama<\/strong>, ali i koliko je va\u017eno razumjeti njihove granice. Neuralne mre\u017ee mogu predvidjeti broj pacijenata za sutra \u2013 no odluka kako te podatke koristiti i dalje ostaje u rukama ljudi.<\/p>

\u017deli\u0161 znati vi\u0161e o AI2MED projektu i budu\u0107im webinarima? Pretplati se na na\u0161 newsletter<\/a> i prvi primaj obavijesti o nadolaze\u0107im eventima. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Kako bolnice mogu predvidjeti broj pacijenata koje \u0107e primiti sutra \u2013 i to s nekoliko redaka koda? Na na\u0161em AI2MED webinaru istra\u017eena je upravo ta tema: primjena neuronskih mre\u017ea u predvi\u0111anju bolni\u010dke zauzetosti i optimizaciji planiranja resursa.<\/p>\n","protected":false},"author":4,"featured_media":19000,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_uf_show_specific_survey":0,"_uf_disable_surveys":false,"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[1],"tags":[63],"class_list":["post-18999","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized-hr","tag-healthtech"],"acf":[],"yoast_head":"\nPredvi\u0111anje zauzetosti bolni\u010dkih kreveta: kako neuronske mre\u017ee poma\u017eu planiranju u bolnicama - Smion<\/title>\n<meta name=\"description\" content=\"Kako bolnice mogu predvidjeti broj pacijenata koje \u0107e primiti sutra \u2013 i to s nekoliko redaka koda? 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