Can we predict psychiatric disorders at the adolescence period in toddlers? A machine learning approach

Usta, Miraç Barış; Karabekiroğlu, Koray; Say, Gökçe Nur; Gümüş, Yusuf Yasin; Aydın, Muazzez; Şahin, Berkan; Bozkurt, Abdullah; Karaosman, Tolga; Aral, Armağan; Çobanoğlu, Cansu; Kurt, Ayşegül Duman; Kesim, Neriman; Şahin, İrem

doi:10.5455/pbs.20190806125540

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…In this study, we contributed to filling this gap in the literature by focusing on the opinions of mental health professionals using AI in therapeutic interventions in Turkey. Given that the use of AI in mental health services in Turkey is still very new (Bilge et al, 2020;Erebak, 2018;Usta et al, 2020), the insights of professionals engaging with this technology are essential to predict the future of AI in the field of mental health. Accordingly, answers to the following questions were sought: RQ1: How do mental health professionals evaluate the advantages and disadvantages of using AI in health care services?…”

Section: Introductionmentioning

confidence: 99%

The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think?

Gültekin,

Şahin

2024

Cyberpsychology

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Artificial intelligence (AI) supported applications have become increasingly prevalent in health care practice, with mental health services being no exception. AI applications can be employed at various stages of mental health services and with different roles. This study aims to understand the potential advantages and disadvantages of using AI in mental health services, to explore its future roles, and outcomes through the opinions of mental health professionals engaged with AI. Thus, we conducted a qualitative study with semi-structured interviews with 13 mental health professionals who have expertise in AI, and a content analysis of the interview transcripts. We concluded that the use of AI in mental health services revealed advantages and disadvantages for clients, the profession itself, and experts. Our study emphasized four findings. Firstly, the participants were likely to have positive opinions about using AI in mental health services. Increased satisfaction, widespread availability of mental health services, reduced expert-driven problems, and workload were among the primary advantages. Secondly, the participants stated that AI could not replace a clinician but could serve a functional role as an assistant. However, thirdly, they were skeptical about the notion that AI would radically transform mental health services. Lastly, the participants expressed limited views on ethical and legal issues surrounding data ownership, the ‘black box’ problem, algorithmic bias, and discrimination. Although our research has limitations, we expect that AI will play an increasingly important role in mental health care services.

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Section: Introductionmentioning

confidence: 99%

The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think?

Gültekin,

Şahin

2024

Cyberpsychology

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“…For an increasing number of health conditions with complex aetiologies, artificial intelligence (AI) has been successfully applied to identify when an individual is at high risk for a future adverse health outcome, e.g., [18,19]. In the discipline of developmental psychology, the machine learning approach of random forests (RF) has been applied to predict future psychiatric conditions [20] and to predict infant growth using inflammatory markers [21]. The present study applied RF to predict psychomotor developmental delay in 9-month-old infants using data on a wide array of factors in pregnancy, birth and early infancy.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Waynforth

2023

Reprod. Med.

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Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. In total, 53 predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 53 variables, birthweight, gestational age at birth, pre-pregnancy BMI, family income and parents’ ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% specificity. No features measuring aspects of early infant care or environment meaningfully added to algorithm performance. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identified in early infancy using machine learning, and that the best predictors are factors present prior to and at birth.

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Artificial Intelligence Applications and Related Technologies in Mental Health Services)

ÜLKER,

AKKAN

2023

Fenerbahçe Üniversitesi Sosyal Bilimler Dergisi

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Dünya nüfusu her geçen gün artmaktadır. Bu durum çeşitli dezavantajları da beraberinde getirmektedir. Özellikle ruh sağlığı alanında hastaların hizmete erişimi ve etkin tedaviye ulaşmasının önünde en gelişmiş ülkelerde dahi çeşitli sorunlar yaşanmaktadır. Nüfus artışına paralel olarak, ruh sağlığı bozulmuş ve terapi süreçlerine ihtiyaç duyan insan sayısı da her geçen gün artmaktadır. Bu konuda yetişmiş uzman sayısı, gelişmiş ülkelerde dahi yeterli ihtiyacı karşılayacak miktarda değildir. Söz konusu problem, teknolojik gelişmeler ışında doğan yapay zeka uygulamaları ile kısmen çözülebilecek gibi gözükmektedir. Her ne kadar bir takım kaygıları ve etik tartışmaları barındırsa da YZ alanındaki hızlı ve başarılı gelişmeler bu konudaki destekleyici sistemlerin de etkisi ile önümüzdeki yıllarda daha da yaygınlaşacağına işaret etmektedir. Yapay zeka teknolojilerinin insana dair hata potansiyeli barındıran fizyolojik çıktılardan bağımsız olması ve sıfır hataya yaklaşması, terapi süreçlerinde terapiste çeşitli kolaylık ve avantajlar sağlaması, daha kısa sürede daha fazla insana terapi desteği sunması, düşük maliyet gibi avantajlarla ruh sağlığı alanında yaygınlaşmasının önünü açacak gibi görülmektedir. Son dönemde sıklıkla adı geçen, dijital fenotipleme, sanal terapistler (robo –psikologlar), sanal gerçeklik, akıllı mobil uygulamalar gibi teknolojiler ile ruh sağlığı alanında yapay zeka uygulamalarının kendisine daha büyük bir alan açtığı görülmektedir. Söz konusu çalışmamız, ruh sağlığı alanında geliştirilmiş yapay zeka teknolojilerinin tarihsel sürecini ele alarak, tanı, tedavi ve rehabilitasyon hizmetlerinde başarılı sonuçlar elde edilmiş teknolojilere değinmektedir.

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Can we predict psychiatric disorders at the adolescence period in toddlers? A machine learning approach

Cited by 3 publications

References 20 publications

The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think?

The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think?

A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Artificial Intelligence Applications and Related Technologies in Mental Health Services)

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