{"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\/nb\/2023\/09\/predicting-self-harm-and-suicide-in-adolescents-using-ml\/","title":{"rendered":"Forutsi selvskading og selvmord hos ungdom ved hjelp av ML"},"content":{"rendered":"<p><strong>Forskere fra School of Clinical Medicine ved University of New South Wales har brukt maskinl\u00e6ring (ML) til \u00e5 utvikle mer n\u00f8yaktige prediktorer for selvskading og selvmord hos ungdom.<\/strong><\/p>\n<p>Den psykiske helsen blant ungdom har g\u00e5tt tilbake p\u00e5 verdensbasis, og den australske statistikken gjenspeiler situasjonen i mange andre samfunn. Selvmord er den vanligste d\u00f8ds\u00e5rsaken blant australiere mellom 15 og 24 \u00e5r.<\/p>\n<p>N\u00e5v\u00e6rende prediktorer, som tidligere selvskading eller selvmordsfors\u00f8k, har vist seg \u00e5 v\u00e6re bare litt mer effektive enn \u00e5 stole p\u00e5 tilfeldigheter. Den nye prediktive modellen <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0165178123003967?via%3Dihub\">forskere utviklet ved hjelp av ML<\/a> er betydelig mer effektivt.<\/p>\n<p>Datasettet best\u00e5r av tilbakemeldinger fra sp\u00f8rreskjemaer og intervjuer med 2 809 ungdommer som deltok i Longitudinal Study of Australian Children.<\/p>\n<p>Blant deltakerne rapporterte 5,2% at de hadde fors\u00f8kt \u00e5 beg\u00e5 selvmord minst \u00e9n gang i l\u00f8pet av de siste 12 m\u00e5nedene, og 10,5% rapporterte selvskadende handlinger.<\/p>\n<p>Ved \u00e5 bruke maskinl\u00e6ring til \u00e5 analysere dataene fant forskerne viktige prediktorer som var mer n\u00f8yaktige enn de som tidligere ble brukt av behandlere innen psykisk helse.<\/p>\n<p>Ytelsen til ML-modellen ble m\u00e5lt ved hjelp av Area Under the Curve (AUC). Det er et tall som g\u00e5r fra 0,5 til 1, der 0,5 er en like god gjetning som et myntkast, og 1 er en 100% n\u00f8yaktig prediksjon.<\/p>\n<p>Ved utelukkende \u00e5 basere seg p\u00e5 tidligere selvskading og selvmordsfors\u00f8k oppn\u00e5dde man en AUC p\u00e5 mellom 0,63 og 0,647. Dette var bare litt bedre enn gjetning, og l\u00e5 under 0,7 til 0,8, som anses som akseptabelt for \u00e5 forutsi risiko.<\/p>\n<p>ML-modellens prediktorer oppn\u00e5dde en AUC p\u00e5 mellom 0,722 og 0,74, noe som er betydelig bedre.<\/p>\n<p>Modellen overrasket forskerne, da den viste at tidligere selvskading eller selvmordsfors\u00f8k ikke var en h\u00f8yrisikofaktor, og at milj\u00f8 og foreldrest\u00f8tte spilte en viktigere rolle.<\/p>\n<p>Dr. Lin, en av forskerne, sier: \"Vi fant ut at den unges milj\u00f8 spiller en st\u00f8rre rolle enn vi trodde. Dette er bra med tanke p\u00e5 forebygging, for n\u00e5 vet vi at det er mer vi kan gj\u00f8re for disse personene.\"<\/p>\n<p>Denne anvendelsen av kunstig intelligens i psykisk helsevern vil hjelpe klinikere til \u00e5 bli mer n\u00f8yaktige i vurderingen av risikoutsatte ungdommer og sette inn tiltak tidligere.<\/p>\n<p>Dr. Lin sa: \"Basert p\u00e5 pasientinformasjon kan ML-algoritmen beregne en poengsum for hver enkelt person, og den kan integreres i det elektroniske journalsystemet. Klinikeren kan raskt hente frem denne informasjonen for \u00e5 bekrefte eller justere vurderingen sin.\"<\/p>\n<p>Modellen er ikke klar til \u00e5 rulles ut i praksis enn\u00e5, men de lovende resultatene tyder p\u00e5 at det er en vei det er verdt \u00e5 forf\u00f8lge.<\/p>\n<p>Det finnes mye data i medisinske journaler, og bruk av kunstig intelligens til \u00e5 analysere dem vil utvilsomt f\u00f8re til flere overraskelser for helsepersonellet.<\/p>","protected":false},"excerpt":{"rendered":"<p>Forskere fra School of Clinical Medicine ved University of New South Wales har brukt maskinl\u00e6ring (ML) til \u00e5 utvikle mer n\u00f8yaktige prediktorer for selvskading og selvmord hos ungdom. Den psykiske helsen blant ungdom har g\u00e5tt tilbake p\u00e5 verdensbasis, og den australske statistikken gjenspeiler situasjonen i mange andre land. Selvmord er den viktigste d\u00f8ds\u00e5rsaken blant australiere mellom 15 og 24 \u00e5r. Dagens prediktorer, som tidligere selvskading eller selvmordsfors\u00f8k, har vist seg \u00e5 v\u00e6re bare litt mer effektive enn \u00e5 stole p\u00e5 tilfeldigheter. Den nye prediksjonsmodellen som forskerne har utviklet ved hjelp av ML, er betydelig mer effektiv. Den<\/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\/nb\/2023\/09\/predicting-self-harm-and-suicide-in-adolescents-using-ml\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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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