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

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Adolescent Suicide and Challenges in PhD Dissertation – Application of

Machine Learning

Dr. Nancy Agnes, Head,

Technical Operations, Phdassistance

[email protected]

Keywords:

Machine Learning Techniques in PhD

Dissertation, Adolescent suicide &

challenges Help in PhD, Machine

Learning Dissertation Help, Adolescent

Suicide, Artificial Intelligence Help,

Computer Science Dissertation Help,

Dissertation Coding & Algorithm

Development and Implementation Services,

Computer Science Research, Machine

Learning (ML) Algorithms, Dissertation

Writing Help, Computer Programming

Languages, Adolescent Suicide, Prevention

Strategies, Machine Learning,

Computational Methodology, Suicide Risk,

Public Health

I. INTRODUCTION

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. 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. 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).

II. IMPACT OF MACHINE LEARNING

Artificial Intelligence (AI) and Machine

Learning (ML) have emerged as essential

tools to investigate large sets of data and

enhance the detection of risk. Thesis

Techniques have attracted the attention of

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. 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

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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. 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).

Fig 1: Basic Steps Involved in Machine Learning

Based Therapy for Suicide Prevention

III. RECENT RESEARCH AROUND THIS

FIELD

In recent research, Haroz et al. combined

machine learning with community-based

suicide surveillance to help identify those at

risk. Their study proves the ability of

Machine Learning 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. The results of

this study can be used as a foundation for

further research on adolescent suicide

(Gradus 2020).

Hill et al. applied classification tree analysis

to prospectively determine suicide

attempters within a huge adolescent

community sample. They concluded that the

tree methodology can act as a powerful tool

to identify individuals at suicide risk.

According to this study, the classification

tree analysis can generate easy-to-implement

decision rules and customized screening

procedures (Hill 2019). 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).

IV. FUTURE SCOPE

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). 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

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large scale can further elevate the prevention

and management strategies involved in

adolescent suicide.

REFERENCES

1. Bernert, R. A., Hilberg, A. M., Melia, R., Kim, J. P., Shah, N. H., & Abnousi, F. (2020). Artificial

intelligence and suicide prevention: a systematic

review of machine learning investigations.

International journal of environmental research and

public health,17(16), 5929.

2. Cohen, J., Wright-Berryman, J., Rohlfs, L., Wright, D., Campbell, M., Gingrich, D.,… & Pestian, J.

(2020). A feasibility study using a machine learning

suicide risk prediction model based on open-ended

interview language in adolescent therapy sessions.

International journal of environmental research and

public health, 17(21), 8187.

3. Forte, A., Sarli, G., Polidori, L., Lester, D., & Pompili, M.(2021). The role of new technologies to prevent

suicide in adolescence: A systematic review of the

literature. Medicina, 57(2),109.

4. Gradus, J. L., Rosellini, A. J., Horvath-Puho, E., Street, A. E., Galatzer-Levy, I., Jiang, T., … &

Sorensen, H. T. (2020). Prediction of sex-specific

suicide risk using machine learning and single-payer

health care registry data from Denmark. JAMA

psychiatry, 77(1),25-34.

5. Haroz, E. E., Walsh, C. G., Goklish, N., Cwik, M. F., O’Keefe, V., & Barlow, A. (2020). Reaching those at

highest risk of suicide: Development of a model using

machine learning methods for use with Native

American communities. Suicide and Life- Threatening

Behavior, 50(2), 422-436.

6. Hill, R. M., Oosterhoff, B., & Do, C. (2020). Using machine learning to identify suicide risk: a

classification tree approach to prospectively identify

adolescent suicide attempters. Archives of suicide

research, 24(2), 218-235.

7. Navarro, M. C., Ouellet-Morin, I., Geoffroy, M. C., Boivin, M., Tremblay, R. E., Cote, S. M., & Orri, M.

(20210. Machine learning assessment of early life

factors predicting suicide attempt in adolescence or

young adulthood. JAMA network open, 4(3),

e211450-e211450.

8. Roy, A., Nikolitch, K., McGinn, R. Et al. (2020). A machine learning approach predicts future risk at

suicidal ideation from social media data. Npj Digit.

Med. 3,78.

9. Su, C., Aseltine, R., Doshi, R., Chen, K., Rogers, S. C., & Wang, F. (2020). Machine learning for suicide

risk prediction in children and adolescents with

electronic health records. Translational psychiatry,

10(1), 1-10.

10. World Health Organization ( 2019, 02 September “SUICIDE.” Retrieved from

https://www.who.int/news-room/fact-

sheets/detail/suicide
https://www.who.int/news-room/fact-sheets/detail/suicidehttps://www.who.int/news-room/fact-sheets/detail/suicide

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