{"id":2758,"date":"2020-01-30T23:18:49","date_gmt":"2020-01-30T23:18:49","guid":{"rendered":"http:\/\/orionhealth-blog.local\/?p=2758"},"modified":"2025-03-28T01:12:26","modified_gmt":"2025-03-28T01:12:26","slug":"predicting-suicide-risk-with-machine-learning","status":"publish","type":"post","link":"https:\/\/orionhealth.com\/ca\/blog\/predicting-suicide-risk-with-machine-learning\/","title":{"rendered":"Predicting suicide risk with machine learning"},"content":{"rendered":"<p>According to the&nbsp;<a rel=\"noreferrer noopener\" href=\"https:\/\/www.who.int\/whr\/2001\/media_centre\/press_release\/en\/\" target=\"_blank\">World Health Organization<\/a>, around 450 million people currently suffer from such conditions, placing mental disorders among the leading causes of ill-health and disability worldwide.&nbsp;&nbsp;<\/p>\n\n\n\n<p>The emerging field of&nbsp;Predictive&nbsp;Medicine&nbsp;in mental health has recently generated tremendous interest. An increasing body of genetic and imaging research shows that it is becoming possible&nbsp;to&nbsp;accurately&nbsp;forecast the onset of major psychiatric disorders, such as depression and schizophrenia before people become sick.&nbsp;<\/p>\n\n\n\n<p>Can machine learning and algorithms be&nbsp;harnessed&nbsp;meaningfully in the area of mental health?&nbsp;Can technology help predict mental illnesses and somehow&nbsp;positively contribute&nbsp;to the diagnoses or treatment?&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Global burden of suicide&nbsp;&nbsp;<\/h4>\n\n\n\n<p>Globally, close to&nbsp;800,000&nbsp;people die by suicide every year, making suicide the 15<sup>th<\/sup>&nbsp;leading cause of death worldwide.&nbsp;&nbsp;<\/p>\n\n\n\n<p>The public health implications of such&nbsp;suicide&nbsp;events&nbsp;equate&nbsp;to more than 20 million attempted suicides per year worldwide.&nbsp;&nbsp;<\/p>\n\n\n\n<p>For each suicide, there are more than 20 suicide attempts. Suicide and&nbsp;self-harm&nbsp;attempts have&nbsp;cascading detrimental effects that negatively impact the individual\u2019s family and&nbsp;their community.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Can&nbsp;suicide be predicted and prevented effectively?&nbsp;&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Suicide prediction that can lead to prevention&nbsp;<\/h4>\n\n\n\n<p>Early identification of individuals likely to cause harm to themselves will enable mental health caregivers to intervene early before a serious incident takes place. Early intervention means these at-risk patients can receive the right mental healthcare they need to avoid serious events, including suicide.&nbsp;&nbsp;<\/p>\n\n\n\n<p>A&nbsp;<a href=\"https:\/\/ajp.psychiatryonline.org\/doi\/full\/10.1176\/appi.ajp.2018.17101167?journalCode=ajp\" target=\"_blank\" rel=\"noreferrer noopener\">study<\/a>&nbsp;featured in the&nbsp;<a href=\"https:\/\/ajp.psychiatryonline.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">American Journal of Psychiatry<\/a>&nbsp;sought to develop and validate models using electronic health records to predict suicide&nbsp;attempts&nbsp;and suicide&nbsp;deaths&nbsp;following an outpatient visit.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Combining electronic health record (EHR) data and results from a depression&nbsp;questionnaire,&nbsp;the&nbsp;study showed that an&nbsp;accurate predictive analytics model&nbsp;can&nbsp;predict suicide risk in the 90 days following a mental health visit.&nbsp;&nbsp;<\/p>\n\n\n\n<p>The team found that in the 90 days following a mental health visit, suicide&nbsp;attempts and deaths among patients in the&nbsp;top one&nbsp;percent of predicted risk were 200 times more common&nbsp;than&nbsp;among those in the bottom half of&nbsp;predicted&nbsp;risk.&nbsp;<\/p>\n\n\n\n<p>The study identified that strongest predictors of a self-harm attempt included prior suicide attempts, the state of their current mental health, substance abuse diagnoses, use of&nbsp;prescribed&nbsp;psychiatric medications, inpatient or emergency room care and high scores on the depression questionnaire.&nbsp;&nbsp;<\/p>\n\n\n\n<p>More accurate predictive analytics&nbsp;can be determined, by drawing out meaningful data from existing patient datasets to build prediction models that analyse other risk factors such as&nbsp;early-life characteristics, personality type, psychosocial stress and social&nbsp;adversity&nbsp;and so on.&nbsp;<\/p>\n\n\n\n<p>Having a comprehensive view could also help mental healthcare providers identify&nbsp;whether&nbsp;they should help patents develop a personal safety plan or counsel them about reducing access to means of self-harm.&nbsp;&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Integrating prediction models and data sets for a better solution&nbsp;<\/h4>\n\n\n\n<p>There have been several advances in this space of predictive analysis globally. But we can do better!&nbsp;&nbsp;<\/p>\n\n\n\n<p>Globally,&nbsp;a more accurate prediction model can be created&nbsp;if&nbsp;all&nbsp;health systems work&nbsp;together to&nbsp;integrate prediction models into an existing process for identifying and addressing suicide risk.&nbsp;<\/p>\n\n\n\n<p>Risk predictions can supplement clinical judgement and direct\u00a0clinicians&#8217;\u00a0attention to where it is most needed. Predictions do not replace a clinical\u00a0assessment, but they can help providers intervene with the right patients at the right time.\u00a0\u00a0<\/p>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/orionhealth.com\/\/nz\/about-us\/contact-an-expert\/\">Contact an expert<\/a><\/div>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>According to the&nbsp;World Health Organization, around 450 million people currently suffer from such conditions, placing mental disorders among the leading causes of ill-health and disability worldwide.&nbsp;&nbsp; The emerging field of&nbsp;Predictive&nbsp;Medicine&nbsp;in mental health has recently generated tremendous interest. An increasing body of genetic and imaging research shows that it is becoming possible&nbsp;to&nbsp;accurately&nbsp;forecast the onset of major [&hellip;]<\/p>\n","protected":false},"author":35,"featured_media":2759,"comment_status":"closed","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[1],"tags":[44],"region":[25,26,23,24,27,28],"class_list":["post-2758","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-population-health-management","region-au","region-ca","region-global","region-nz","region-uk","region-us"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predicting suicide risk with machine learning - Orion Health<\/title>\n<meta name=\"description\" content=\"Can technology help predict mental illnesses and somehow positively contribute to the diagnoses or treatment?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/orionhealth.com\/ca\/blog\/predicting-suicide-risk-with-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting suicide risk with machine learning - 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