{"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\/da\/2023\/09\/predicting-self-harm-and-suicide-in-adolescents-using-ml\/","title":{"rendered":"Forudsigelse af selvskade og selvmord hos unge ved hj\u00e6lp af ML"},"content":{"rendered":"<p><strong>Forskere fra School of Clinical Medicine ved University of New South Wales har brugt maskinl\u00e6ring (ML) til at udvikle mere pr\u00e6cise pr\u00e6diktorer for selvskade og selvmord hos unge.<\/strong><\/p>\n<p>Unges mentale sundhed er faldet p\u00e5 verdensplan, og de australske statistikker er en rimelig afspejling af dem i mange andre samfund. Selvmord er den st\u00f8rste d\u00f8ds\u00e5rsag blandt australiere mellem 15 og 24 \u00e5r.<\/p>\n<p>Nuv\u00e6rende pr\u00e6diktorer som tidligere selvskade eller selvmordsfors\u00f8g har vist sig kun at v\u00e6re lidt mere effektive end at stole p\u00e5 tilf\u00e6ldigheder. Den nye forudsigelsesmodel <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0165178123003967?via%3Dihub\">forskere udviklet ved hj\u00e6lp af ML<\/a> er betydeligt mere effektiv.<\/p>\n<p>Datas\u00e6ttet bestod af feedback fra sp\u00f8rgeskemaer og interviews med 2.809 unge deltagere i Longitudinal Study of Australian Children.<\/p>\n<p>Blandt deltagerne rapporterede 5,2% om selvmordsfors\u00f8g mindst \u00e9n gang i l\u00f8bet af de foreg\u00e5ende 12 m\u00e5neder, og 10,5% rapporterede om selvskadende handlinger.<\/p>\n<p>Ved at bruge maskinl\u00e6ring til at analysere dataene fandt forskerne vigtige pr\u00e6diktorer, der var mere pr\u00e6cise end dem, der tidligere blev brugt af psykiatere.<\/p>\n<p>ML-modellens ydeevne blev m\u00e5lt med AUC-metrikken (Area Under the Curve). Det er et tal, der g\u00e5r fra 0,5 til 1, hvor 0,5 er et lige s\u00e5 godt g\u00e6t som et m\u00f8ntkast, og 1 er en 100% n\u00f8jagtig forudsigelse.<\/p>\n<p>Ved udelukkende at stole p\u00e5 tidligere selvskade og selvmordsfors\u00f8g opn\u00e5ede man en AUC p\u00e5 mellem 0,63 og 0,647. Det var kun lidt bedre end at g\u00e6tte og faldt under det interval p\u00e5 0,7 til 0,8, der anses for at v\u00e6re acceptabelt til at forudsige risiko.<\/p>\n<p>ML-modellens pr\u00e6diktorer opn\u00e5ede en AUC p\u00e5 mellem 0,722 og 0,74, hvilket er betydeligt bedre.<\/p>\n<p>Modellen overraskede forskerne, da den viste, at tidligere selvskade eller selvmordsfors\u00f8g ikke var en h\u00f8jrisikofaktor, og at milj\u00f8 og for\u00e6ldrest\u00f8tte spillede en vigtigere rolle.<\/p>\n<p>Dr. Lin, en af forskerne, sagde: \"Vi fandt ud af, at den unges milj\u00f8 spiller en st\u00f8rre rolle, end vi troede. Det er godt set ud fra et forebyggelsessynspunkt, fordi vi nu ved, at der er mere, vi kan g\u00f8re for disse personer.\"<\/p>\n<p>Denne anvendelse af AI i mental sundhedspleje vil hj\u00e6lpe klinikere med at v\u00e6re mere pr\u00e6cise i vurderingen af unge i risiko og foretage tidligere indgreb.<\/p>\n<p>Dr. Lin sagde: \"Baseret p\u00e5 patientoplysninger kan ML-algoritmen beregne en score for hver person, og den kan integreres i det elektroniske journalsystem. Klinikeren kunne hurtigt hente disse oplysninger for at bekr\u00e6fte eller justere sin vurdering.\"<\/p>\n<p>Modellen er ikke klar til at blive rullet ud i praksis endnu, men de lovende resultater viser, at det er en vej, der er v\u00e6rd at forf\u00f8lge.<\/p>\n<p>Der ligger en masse data i l\u00e6gejournaler, og hvis man bruger AI til at analysere dem, vil det utvivlsomt give sundhedspersonalet flere overraskelser.<\/p>","protected":false},"excerpt":{"rendered":"<p>Forskere fra School of Clinical Medicine ved University of New South Wales har brugt maskinl\u00e6ring (ML) til at udvikle mere pr\u00e6cise pr\u00e6diktorer for selvskade og selvmord hos unge. Unges mentale sundhed er g\u00e5et tilbage p\u00e5 verdensplan, og de australske statistikker er en rimelig afspejling af dem i mange andre samfund. Selvmord er den st\u00f8rste d\u00f8ds\u00e5rsag blandt australiere mellem 15 og 24 \u00e5r. Nuv\u00e6rende pr\u00e6diktorer som tidligere selvskade eller selvmordsfors\u00f8g har vist sig kun at v\u00e6re lidt mere effektive end at stole p\u00e5 tilf\u00e6ldigheder. Den nye forudsigelsesmodel, som forskerne har udviklet ved hj\u00e6lp af ML, er betydeligt mere 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\/da\/2023\/09\/predicting-self-harm-and-suicide-in-adolescents-using-ml\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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