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Adolescent Suicide and Challenges in PhD Dissertation – Application of Machine Learning - Phdassistance

Adolescent suicide has emerged as a challenging public health problem in the last decade. The recent pandemic and the growing usage of social media may escalate the rate further in the near future. With the Introduction of Technology in almost all aspects of life, medicine has also been transformed by the use of technology-based therapies. Researchers and clinicians have begun experimenting and evaluating the use of technology in the prevention and management of severe conditions. <br><br>Learn More: https://bit.ly/34MEFC9<br>

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Adolescent Suicide and Challenges in PhD Dissertation – Application of Machine Learning - Phdassistance

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  1. ADOLESCENTSUICIDE AND CHALLENGES IN PHDDISSERTATION– APPLICATION OF MACHINELEARNING AnAcademicpresentationby Dr.NancyAgnes,Head,TechnicalOperations,Phdassistance Group www.phdassistance.com Email:info@phdassistance.com

  2. TODAY'SDISCUSSION Outline Introduction Impact of Machine Learning RecentResearcharoundthisField FutureScopes

  3. Introduction According to the worldhealthorganization,one of the most common causes of death among 15-19 yearsold issuicide. One-third of this fraction occurs in low- and middle- incomecountries(WHO, 2019). Adolescence is a period where human beings are vulnerabletotheexternalenvironmentandcan reactpositivelyornegativelytowardsthings happeningaround them. Contd...

  4. Adolescentsuicidehasemergedasachallengingpublichealthprobleminthe lastdecade. Therecentpandemicandthegrowingusageofsocialmediamayescalatethe ratefurther in the nearfuture. WiththeIntroductionofTechnologyin almost all aspects of life, medicine has alsobeentransformed bythe useoftechnology-based therapies. Researchersandclinicianshavebegunexperimentingandevaluatingtheuseof technologyintheprevention andmanagementof severeconditions. Contd...

  5. Asearchstrategybasedstudyfoundthatnewtechnologieswereslowlybecoming easyandadoptedsupporttoolsforthepreventionofsuicideinadolescents. It also suggested the efficiency of telepsychiatry and mobile applications in preventingsuicide(Forte 2021).

  6. Impactof Machine Learning Artificial Intelligence (AI)and Machine Learning (ML) have emerged as essential tools to investigate large sets of data andenhance thedetection ofrisk. ThesisTechniqueshaveattractedtheattentionof researchersfromthementalhealthcommunityand computationalpsychiatry(Navarro 2021). Recent studies have shown promising results for the use of machinelearningin suicideprevention. Contd...

  7. The analysis of social media data through machine learning comes across as a promisingtooltoidentifyenvironmentalfactorsthatcontributetothedevelopment ofsuicidalthoughts andbehaviours inan individual(Bernert2020). An algorithm known as the "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)wasdevelopedtopredictfuturerisktosuicidalthoughtsthroughthe analysisof data availablepublicly on Twitter. This algorithm was found to be successful in distinguishing individuals who had a historyor plan of suicide. It critically succeeded at identifying not only the people who were at risk but also thosewho may likely beat risk. Contd...

  8. However,Ithadonespecificdrawbackofwhetherornotsomeonewilltweetabout suicide(Roy 2020). Anotherstudyfoundtheimplementationofasmartphoneapptobefeasiblewhen recordingspeechinadolescentmentalhealththerapysessions(Cohen2020). Contd...

  9. Recent Research aroundthis Field Inrecentresearch,Harozetal. combined machine learning with community-based suicidesurveillancetohelpidentifythoseatrisk. TheirstudyprovestheabilityofMachineLearning Methodologiesto determine those at high risk in the community(Haroz 2020). Acase-cohortstudyutilizedCARTmodellingand Random forest to develop sex-specific risk models as a suicideprevention strategy. Contd...

  10. The results of this study can be used as a foundation for further research on adolescentsuicide (Gradus 2020). Hill et al. applied classification tree analysis to prospectively determine suicide attempterswithin ahuge adolescentcommunity sample. They concluded that the tree methodology can act as a powerful tool to identify individualsat suicide risk. According to this study, the classification tree analysis can generate easy-to- implementdecisionrulesandcustomizedscreeningprocedures(Hill2019). Contd...

  11. In another paper, researchers developed machine learning models for predicting suicidal behaviour in children and adolescents based on their clinical history, as well asidentifying short- andlong-term risk factors. Their findings depicted the application of EHRs (Electronic health records) as a predictive tool to predict suicide risks among adolescents and children with accuracy (Chang2020).

  12. FutureScopes Thefirstandforemoststeptoreducetherateof adolescent suicide is to identify those at potential risk andprovide themwith help. Despitedecadesofresearch,identificationand understandingofsuicideriskstillremainchallenging. Ifusedappropriately,bothAIandMLcancrucially help identify early detection of suicide risk, treatment developmentandimportantmethodologicalcautions (Bernert2020). Contd...

  13. Further research around this field can help in highlighting the causes impeding the preventionstrategiesand stepsthat canbetaken toovercome them. ResearchersmayuseMachine LearningTechniquestoconsiderhundredsof potential factors at once and decide the most powerful and efficient algorithm to predictanewobservationwithoutmakinganyassumptions(Navarro2021). The development of new machine learning methods and testing these techniques on a large scale can further elevate the prevention and management strategies involvedin adolescent suicide.

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