{"id":5064,"date":"2023-09-05T05:37:23","date_gmt":"2023-09-05T05:37:23","guid":{"rendered":"https:\/\/dailyai.com\/?p=5064"},"modified":"2023-09-05T05:37:23","modified_gmt":"2023-09-05T05:37:23","slug":"predicting-self-harm-and-suicide-in-adolescents-using-ml","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nl\/2023\/09\/predicting-self-harm-and-suicide-in-adolescents-using-ml\/","title":{"rendered":"Voorspellen van zelfbeschadiging en su\u00efcide bij adolescenten met behulp van ML"},"content":{"rendered":"<p><strong>Onderzoekers van de School of Clinical Medicine aan de Universiteit van New South Wales hebben machine learning (ML) gebruikt om nauwkeurigere voorspellers van zelfbeschadiging en zelfmoord bij adolescenten te ontwikkelen.<\/strong><\/p>\n<p>De geestelijke gezondheid van adolescenten is wereldwijd achteruitgegaan en de Australische statistieken weerspiegelen die in veel andere samenlevingen. Zelfmoord is de belangrijkste doodsoorzaak onder Australi\u00ebrs tussen de 15 en 24 jaar.<\/p>\n<p>Huidige voorspellers zoals zelfbeschadiging of zelfmoordpogingen in het verleden zijn slechts iets effectiever gebleken dan te vertrouwen op toeval. Het nieuwe voorspellende model de <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0165178123003967?via%3Dihub\">onderzoekers ontwikkeld met ML<\/a> is aanzienlijk effectiever.<\/p>\n<p>De dataset bestond uit feedback van vragenlijsten en interviews met 2.809 adolescente deelnemers aan de Longitudinal Study of Australian Children.<\/p>\n<p>Onder de deelnemers meldden 5,2% minstens \u00e9\u00e9n zelfmoordpoging te hebben ondernomen in de afgelopen 12 maanden en 10,5% rapporteerden daden van zelfbeschadiging.<\/p>\n<p>Door machine learning te gebruiken om de gegevens te analyseren, vonden de onderzoekers belangrijke voorspellers die nauwkeuriger waren dan de voorspellers die eerder werden gebruikt door mensen die werkzaam zijn in de geestelijke gezondheidszorg.<\/p>\n<p>De prestaties van het ML-model werden gemeten met de Area Under the Curve (AUC) metriek. Dit is een getal vari\u00ebrend van 0,5 tot 1, waarbij 0,5 zo goed is als een gok en 1 een 100% nauwkeurige voorspelling is.<\/p>\n<p>Uitsluitend afgaan op de voorgeschiedenis van zelfbeschadiging en zelfmoordpogingen leverde een AUC tussen 0,63 en 0,647 op. Dit was slechts iets beter dan gissen en viel onder het bereik van 0,7 tot 0,8 dat als acceptabel wordt beschouwd voor het voorspellen van risico.<\/p>\n<p>De voorspellers van het ML-model behaalden een AUC tussen 0,722 en 0,74, wat significant beter is.<\/p>\n<p>Het model verraste de onderzoekers omdat het aantoonde dat eerdere zelfbeschadiging of zelfmoordpogingen geen risicofactor waren en dat de omgeving en steun van de ouders een belangrijkere rol speelden.<\/p>\n<p>Dr. Lin, een van de onderzoekers, zei: \"We ontdekten dat de omgeving van de jongere een grotere rol speelt dan we dachten. Dit is goed vanuit het oogpunt van preventie omdat we nu weten dat we meer kunnen doen voor deze individuen.\"<\/p>\n<p>Deze toepassing van AI in de geestelijke gezondheidszorg zal artsen helpen om risicojongeren nauwkeuriger te beoordelen en eerder in te grijpen.<\/p>\n<p>Dr. Lin zei: \"Gebaseerd op pati\u00ebntinformatie zou het ML-algoritme een score kunnen berekenen voor elke persoon en die zou ge\u00efntegreerd kunnen worden in het systeem van elektronische medische dossiers. De arts zou die informatie snel kunnen opvragen om zijn beoordeling te bevestigen of aan te passen.\"<\/p>\n<p>Het model is nog niet klaar om in de praktijk te worden toegepast, maar de veelbelovende resultaten geven aan dat het de moeite waard is om verder te onderzoeken.<\/p>\n<p>Er zitten veel gegevens in medische dossiers en het gebruik van AI om deze te analyseren zal ongetwijfeld meer verrassingen opleveren voor artsen.<\/p>","protected":false},"excerpt":{"rendered":"<p>Onderzoekers van de School of Clinical Medicine van de Universiteit van New South Wales hebben machine learning (ML) gebruikt om nauwkeurigere voorspellers van zelfbeschadiging en zelfmoord bij adolescenten te ontwikkelen. De geestelijke gezondheid van adolescenten is wereldwijd achteruitgegaan en de Australische statistieken zijn een goede afspiegeling van die in veel andere samenlevingen. Zelfmoord is de belangrijkste doodsoorzaak onder Australi\u00ebrs tussen 15 en 24 jaar. Huidige voorspellers zoals zelfbeschadiging of zelfmoordpogingen in het verleden bleken slechts iets effectiever te zijn dan te vertrouwen op toeval. Het nieuwe voorspellende model dat de onderzoekers ontwikkelden met behulp van ML is aanzienlijk effectiever. De<\/p>","protected":false},"author":6,"featured_media":5092,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[150,204,105],"class_list":["post-5064","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-ai-benefits","tag-healthcare","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predicting self-harm and suicide in adolescents using ML | 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\/2023\/09\/predicting-self-harm-and-suicide-in-adolescents-using-ml\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting self-harm and suicide in adolescents using ML | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from the School of Clinical Medicine at the University of New South Wales have used machine learning (ML) to develop more accurate predictors of self-harm and suicide in adolescents. Adolescent mental health has seen a global decline and Australian statistics are a fair reflection of those in many other societies. Suicide is the leading cause of death among Australians aged between 15 and 24. Current predictors like past self-harm or suicide attempts have been found to be only slightly more effective than relying on chance. The new predictive model the researchers developed using ML is considerably more effective. 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