Adolescent Suicide and Challenges in PhD Dissertation – Application of Machine Learning - Phdassistance
                     ADOLESCENT 
SUICIDE  AND 
CHALLENGES IN  PHD 
DAPIPSLSICEARTITOAN TOIFO  N –
MACHINE 
LEARNING
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S 
DISCUSSION
Outline
Introduction
Impact of Machine Learning  
Recent Research around this 
Field  Future Scopes
Introductio
n According to the world health organization, 
one  of the most common causes of death 
among 15-19  years old is suicide.
One-third of this fraction occurs in low- and 
middle-  income countries (WHO, 2019).
Adolescence is a period where human beings 
are  vulnerable to the external environment 
and can  react positively or negatively 
towards things  happening around them.
Contd...
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 allaspects 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.
Contd...
A search strategy based study found that new technologies were slowly 
becoming  easy and adopted support tools for the prevention of suicide 
in adolescents.
It also suggested the efficiency of telepsychiatry
and mobile applications in  preventing suicide(Forte 2021).
Impact 
of  
Artificial Intelligence (AI) and Machine Learning (ML) 
Machine have  emerged as essential tools to investigate 
 large sets of data  and enhance the detection of 
risk.
Learnin Thesis Techniques have attracted the attention of  
g researchers from the mental health community and  
computational psychiatry (Navarro 2021).
Recent studies have shown promising results for the 
use of  machine learning in suicide prevention.
Contd...
The analysis of social media data through machine learning comes 
across as a  promising tool to identify environmental factors that 
contribute to the development  of suicidal thoughts and behaviours in an 
individual (Bernert 2020).
An algorithm known as the "Suicide Artificial Intelligence Prediction 
Heuristic  (SAIPH) was developed to predict future risk to suicidal 
thoughts through the  analysis of data available publicly on Twitter.
This algorithm was found to be successful in distinguishing individuals 
who had a  history or plan of suicide.
It critically succeeded at identifying not only the people who were at risk 
but also  those who may likely be at risk.
Contd...
However, It had one specific drawback of whether or not someone will 
tweet about  suicide (Roy 2020).
Another study found the implementation of a smartphone app to be 
feasible when  recording speech in adolescent mental health therapy 
sessions(Cohen 2020).
Contd...

Recent  In recent research, Haroz et al.
Research  
around combined machine
learning with
this  Field community-based  suicide surveillance to help 
iTdheenirt ifsyt utdhyo sper oavt eriss kth. e ability of Machine Learnin
g  Methodologies to determine those at high risk 
in the  community (Haroz 2020).
A case-cohort study utilized CART modelling and  
Random forest to develop sex-specific risk 
models as a  suicide prevention strategy.
Contd...
The resultsof this studycan be used as a foundation for further
research on  adolescent suicide (Gradus 2020).
Hill et al. appliedclassification tree analysis to
prospectively determine suicide  attempters within a huge 
adolescent community sample.
They concluded that the treemethodology can act asa
powerful tool to identify  individuals at suicide risk.
Accordingto this study, the classification tree analysis can
generate easy-to-  implement decision rules and customized screening 
procedures (Hill 2019).
Contd...
In another paper, researchers developed machine learning models for 
predicting  suicidal behaviour in children and adolescents based on their 
clinical history, as well  as identifying short- and long-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  (Chang 2020).
Future Scopes
The first and foremost step to reduce the rate of 
 adolescent suicide is to identify those at 
potential risk  and provide them with help.
Despite decades of research, identification and  
understanding of suicide risk still remain 
challenging.
If used appropriately, both AI and ML can 
crucially  help identify early detection of suicide 
risk, treatment  development and important 
methodological cautions  (Bernert 2020).
Contd...
Further research around this field can help in highlighting the causes 
impeding the  prevention strategies and steps that can be taken to 
overcome them.
Researchers may use Machine Learning Techniques to consider hundreds 
of  potential factors at once and decide the most powerful and efficient 
algorithm to  predict a new observation without making any assumptions 
(Navarro 2021).
The development of new machine learning methods and testing these 
techniques  on a large scale can further elevate the prevention and 
management strategies  involved in adolescent suicide.
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