{"id":12979,"date":"2024-06-20T16:15:39","date_gmt":"2024-06-20T16:15:39","guid":{"rendered":"https:\/\/dailyai.com\/?p=12979"},"modified":"2024-06-21T15:12:06","modified_gmt":"2024-06-21T15:12:06","slug":"ai-system-can-predict-how-anxious-you-are-from-reactions-to-photos","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nl\/2024\/06\/ai-system-can-predict-how-anxious-you-are-from-reactions-to-photos\/","title":{"rendered":"AI-systeem kan aan de hand van reacties op foto's voorspellen hoe angstig je bent"},"content":{"rendered":"

Stel je voor dat je iemands angstniveau kunt voorspellen door hem een paar plaatjes te laten beoordelen en een paar eenvoudige vragen te laten beantwoorden. <\/strong><\/p>\n

Dat is precies wat onderzoekers van de Universiteit van Cincinnati en Northwestern University hebben bereikt met hun \"Comp Cog AI\"-systeem.\u00a0<\/span><\/p>\n

Door AI te combineren met de wetenschap van hoe ons brein informatie verwerkt, hebben ze een hulpmiddel gemaakt dat nauwkeurig mensen kan identificeren die mogelijk worstelen met angst.\u00a0<\/span><\/p>\n

De <\/span>onderzoek<\/span><\/a>gepubliceerd in Mental Health Research, waren meer dan 3000 deelnemers uit de hele VS bij betrokken. <\/span><\/p>\n

Elke persoon beoordeelde een reeks licht emotionele beelden van het International Affective Picture System (IAPS) en gaf basisinformatie over zichzelf, zoals leeftijd en waargenomen eenzaamheid.\u00a0<\/span><\/p>\n

IAPS werd ontwikkeld door het Centrum voor de Studie van Emotie en Aandacht aan de Universiteit van Florida. Het biedt een gestandaardiseerde set foto's die beoordeeld worden op hun emotionele inhoud in termen van valentie (aangenaamheid), arousal (intensiteit) en dominantie (controle).<\/p>\n

\"Ai
Een voorbeeld van een afbeelding uit het International Affective Picture System (IAPS). Bron: Mental Health Research.<\/figcaption><\/figure>\n

Het AI-systeem analyseerde vervolgens deze gegevens en zocht naar patronen in de manier waarop mensen op de afbeeldingen reageerden en hoe deze reacties gerelateerd waren aan hun angstniveaus.\u00a0<\/span><\/p>\n

Na training was het Comp Cog AI-systeem in staat om angst te voorspellen met een nauwkeurigheid tot 81%. Dit biedt hoop voor een toekomst waarin geestelijke gezondheidsproblemen kunnen worden ge\u00efdentificeerd met behulp van lichtgewicht zelfbedieningssystemen.\u00a0<\/span><\/p>\n

Hoofdauteur Sumra Bari <\/span>legt uit<\/span><\/a>We gebruikten minimale computermiddelen en een kleine set variabelen om angstniveaus te voorspellen. Een belangrijke set van deze variabelen kwantificeert processen die belangrijk zijn voor het beoordelingsvermogen.\"<\/span><\/p>\n

Meer over het onderzoek<\/h2>\n

Hier lees je meer over hoe het onderzoek in zijn werk ging:<\/p>\n

    \n
  1. Gegevensverzameling<\/b>: Deelnemers vulden een taak in waarbij ze 48 licht emotionele afbeeldingen uit IAPS beoordeelden van -3 (zeer onaardig) tot +3 (zeer goed). Ze beantwoordden ook vragen over hun leeftijd, ervaren eenzaamheid en demografische informatie.<\/span><\/li>\n
  2. Eigenschapsextractie<\/b>: Het AI-systeem haalde 15 belangrijke beoordelingsvariabelen uit de beoordelingsgegevens van de foto's, zoals verliesaversie, risicovermijding en consistentie tussen beloning en afkeer. Deze variabelen kwantificeren vertekeningen in de beoordeling van beloning\/afkeer en zijn gekoppeld aan hersensystemen die betrokken zijn bij zowel beoordeling als angst.<\/span><\/li>\n
  3. AI-training en voorspelling<\/b>: De onderzoekers gebruikten Random Forest en gebalanceerde Random Forest machine learning algoritmen om het AI-systeem te trainen op een subset van de gegevens. De AI gebruikte de beoordelingsvariabelen en contextuele factoren om het angstniveau van elke deelnemer te voorspellen, zoals gemeten door het state anxiety-gedeelte van de State-Trait Anxiety Inventory (STAI).<\/span><\/li>\n
  4. Modelevaluatie en -interpretatie<\/b>: Het getrainde AI-systeem werd getest op de overgebleven gegevens om de nauwkeurigheid, gevoeligheid en specificiteit bij het voorspellen van angstniveaus te beoordelen. De onderzoekers voerden ook bemiddelings- en moderatieanalyses uit om te begrijpen hoe de beoordelingsvariabelen en contextuele factoren op elkaar inwerkten om angst te modelleren.<\/span><\/li>\n<\/ol>\n

    De vier belangrijkste voorspellers - leeftijd, eenzaamheid, huishoudinkomen en werkstatus - droegen 29-31% bij aan de voorspellende kracht van het model, terwijl de 15 beoordelingsvariabelen gezamenlijk 55-61% bijdroegen.<\/span><\/p>\n

    Co-senior auteur Aggelos Katsaggelos benadrukte het belang van de aanpak van het onderzoek door te zeggen: \"Het gebruik van een taak voor het beoordelen van afbeeldingen met contextuele variabelen die het oordeel be\u00efnvloeden lijkt misschien eenvoudig, maar door patronen in voorkeur te begrijpen, kunnen we de kritieke componenten voor een grote set gedragingen blootleggen.\"<\/span><\/p>\n

    De onderzoekers willen de Comp Cog AI-technologie ontwikkelen tot een gebruiksvriendelijke app voor zorgverleners, ziekenhuizen en zelfs het leger om snel individuen met een hoog risico op angst te identificeren.\u00a0<\/span><\/p>\n

    Zoals Bari opmerkt: \"De plaatjeswaarderingstaak kan gebruikt worden om dagelijkse en onbevooroordeelde momentopnames te maken van iemands geestelijke gezondheidsstatus zonder directe vragen te stellen die negatieve of verontrustende gevoelens kunnen oproepen.\"\u00a0<\/span><\/p>\n

    Eerder onderzoek maakte gebruik van AI om schizofrenie helpen diagnosticeren<\/a>terwijl er hulpmiddelen zijn ontwikkeld om AI-therapie te bieden aan mensen met psychische aandoeningen door middel van digitale avatars<\/a>.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Stel je voor dat je iemands angstniveau kunt voorspellen door hem een paar foto's te laten beoordelen en een paar eenvoudige vragen te laten beantwoorden. Dat is precies wat onderzoekers van de Universiteit van Cincinnati en Northwestern University hebben bereikt met hun \"Comp Cog AI\"-systeem. Door AI te combineren met de wetenschap van hoe ons brein informatie verwerkt, hebben ze een hulpmiddel gemaakt dat nauwkeurig mensen kan identificeren die mogelijk worstelen met angst. Aan het onderzoek, gepubliceerd in Mental Health Research, deden meer dan 3000 deelnemers uit de hele VS mee. Elke persoon beoordeelde een reeks licht emotionele beelden van het International Affective Picture System (IAPS) en<\/p>","protected":false},"author":2,"featured_media":13004,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[178],"class_list":["post-12979","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-medicine"],"yoast_head":"\nAI system can predict how anxious you are from reactions to photos | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nl\/2024\/06\/ai-system-can-predict-how-anxious-you-are-from-reactions-to-photos\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI system can predict how anxious you are from reactions to photos | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Imagine being able to predict someone’s anxiety level just by having them rate a few pictures and answer some simple questions. That’s exactly what researchers from the University of Cincinnati and Northwestern University have accomplished with their “Comp Cog AI” system.\u00a0 By combining AI with the science of how our minds process information, they’ve created a tool that can accurately identify people who might be struggling with anxiety.\u00a0 The study, published in Mental Health Research, involved over 3,000 participants from across the US. 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