Top

Journal of Child and Family Studies

Gepubliceerd in:

12-12-2019 | Original Paper

Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences

Auteurs: Mihaela Robila, Stefan A. Robila

Gepubliceerd in: Journal of Child and Family Studies | Uitgave 10/2020

Abstract

Objectives

Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness.

Methods

The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families.

Results

The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services.

Conclusions

Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.

vorige artikel A Closer Look at Sexual Passion in Relationships

volgende artikel Effects of Socialization on Adolescent Sexual Behaviors in Lao PDR

Afzali, M. H., Sunderland, M., Stewart, S., Masse, B., Seguin, J., Newton, N., Teesson, M., & Conrod, P. (2019). Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation. Addiction, 114(4), 662671. https://doi.org/10.1111/add.14504.CrossRef

AI for Good Summit. (2018). UN Geneva, Switzerland. https://www.itu.int/en/ITUT/AI/2018/Pages/default.aspx.

AI Social Good Services. (2019). Transforming social services How cognitive technology is helping to protect the most vulnerable. https://www.ibm.com/watson/advantage-reports/ai-socialgood-social-services.html.

Ahn, W., & Vassileva, J. (2016). Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug and Alcohol Dependence, 161, 247–257.PubMedPubMedCentral

Amrit, C., Paauw, T., Aly, R., & Lavric, M. (2017). Identifying child abuse through text mining and machine learning. Expert Systems with Applications: An International Journal, 88(C), 402–418.

Askland, K. D., Garnaat, S., Sibrava, N. J., Boisseau, C. L., Strong, D., Mancebo, M., Greenberg, B., Rasmussen, S., & Eisen, J. (2015). Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. International Journal of Methods in Psychiatric Research, 24(2), 156–169.PubMedPubMedCentral

Battista, P., Salvatore, C., & Castiglioni, I. (2017). Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behavioural Neurology, 1850909. https://doi.org/10.1155/2017/1850909.

Bedaf, S., Gelderblom, G. J., & De Witte, L. (2015). Overview and categorization of robots supporting independent living of elderly people: what activities do they support and how far have they developed. Assistive Technology, 27(2), 88–100.PubMed

Bose, E., Maganti, S., Bowles, K. H., Brueshoff, B. L., & Monsen, K. A. (2019). Machine learning methods for identifying critical data elements in nursing documentation. Nursing Research, 68(1), 65–72.PubMed

Bostrom, N., & Yudkowski, E. (2014). The ethics or artificial Intelligence. In F. William & M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 316–330). Cambridge, UK: Cambridge University Press.

Camp, L. J., & Huber, L. L. (2017). Privacy implications of aware, active, and adaptive technologies. In S. Kwon (Ed), Gerontechnology: research, practice, and principles in the field of technology and aging (pp. 91–114). New York, NY: Springer Publishing.

Chang, T. S., Coen, M. H., Rue, A. L., Jonaitis, E., Koscik, R., Hermann, B., & Sager, M. (2012). Machine learning amplifies the effect of parental family history of Alzheimer’s disease on list learning strategy. Journal of the International Neuropsychological Society, 18(3), 428–439.PubMedPubMedCentral

Chen, Y., Argentinis, J. E., & Weber, G. (2016). IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clinical Therapeutics, 38(4), 688–701.PubMed

Chen, H. Y., Hou, T. W., & Chuang, C. H., TBPS Research Group. (2010). Applying data mining to explore the risk factors of parenting stress. Expert Systems with Applications, 37(1), 598–601.

Cho, S. H., & Lee, S. L. (2018). Prediction model for children's cognitive development using machine learning techniques. International Information Institute (Tokyo). Information, 21(1), 123–130.

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.PubMed

Cormen, T. H., Leiserson, C. E., Rivest, R. L. & Stein, C. (2009). Introduction to Algorithms. Cambridge, MA: MIT Press.

Cornet, G. (2013). Robot companions and ethics a pragmatic approach of ethical design. International Journal of Bioethics, 24(4), 49–58.PubMed

Crutzen, R., Giabbanelli, P. J., Jander, A., Mercken, L., & de Vries, H. (2015). Identifying binge drinkers based on parenting dimensions and alcohol-specific parenting practices: building classifiers on adolescent-parent paired data. BMC Public Health, 15(1), 747.PubMedPubMedCentral

Ertel, W. (2018). Introduction to artificial intelligence. New York, NY: Springer.

Gawande, N. A., Daily, J. A., Siegel, C., Tallent, N. R., & Vishnu, A. (2018). Scaling deep learning workloads: Nvidia dgx-1/pascal and intel knights landing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.04.073.CrossRef

Gillingham, P. (2016). Predictive risk modeling to prevent child maltreatment and other adverse outcomes for service users: inside the ‘black box’ of machine learning. British Journal of Social Work, 46(4), 1044–1058.PubMed

Gillingham, P. (2017). Predictive risk modelling to prevent child maltreatment: insights and implications from Aotearoa/New Zealand. Journal of Public Child Welfare, 11(2), 150–165.

Gradus, J. L., King, M. W., Galatzer-Levy, I., & Street, A. E. (2017). Gender differences in machine learning models of trauma and suicidal ideation in veterans of the Iraq and Afghanistan wars. Journal of Traumatic Stress, 30(4), 362–371.PubMedPubMedCentral

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.

Hamet., P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism Clinical and Experimental, 69, 36–40.

Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., ... & Zwitter, A. (2019). Will democracy survive big data and artificial intelligence? In D. Helbing (Ed.), Towards digital enlightenment (pp. 73–98). Cham: Springer.

Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266.PubMed

Ho, T. K. (1995). Random decision forests. In Proceedings of the Third IEEE International Conference on Document Analysis and Recognition, 1, 278–282.

Hong, B., Malik, A., Lundquist, J., Bellach, I., & Kontokosta, C. E. (2018). Applications of machine learning methods to predict readmission and length-of-stay for homeless families: the case of WIN shelters in New York City. Journal of Technology in Human Services, 36(1), 89–104.

Huijnen, C. A. G. J., Lexis, M. A. S., & de Witte, L. P. (2017). Robots as new tools in therapy and education for children with autism. International Journal of Neurorehabilitation, 4, 278.

Ioannidis, K., Chamberlain, S. R., Treder, M. S., Kiraly, F., Leppink, E. W., Redden, S. A., Stein, D. J., Lochner, C., & Grant, J. E. (2016). Problematic internet use (PIU): associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research, 83, 94–102.PubMedPubMedCentral

Insel, T. R., Landis, S. C., & Collins, F. S. (2013). The NIH brain initiative. Science, 340(6133), 687–688.PubMedPubMedCentral

Joel, S., Eastwick, P. W., & Finkel, E. J. (2017). Is romantic desire predictable? Machine learning applied to initial romantic attraction. Psychological Science, 28(10), 1478–1489.PubMed

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.

Kessler, R. C., van Loo, H. M., Wardenaar, K. J., Bossarte, R. M., Brenner, L. A., Cai, T., & Zaslavsky, A. M. (2016). Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Molecular Psychiatry, 21(10), 1366–1371.PubMedPubMedCentral

Kornfield, R., Sarma, P. K., Shah, D. V., McTavish, F., Landucci, G., Pe-Romashko, K., & Gustafson, D. H. (2018). Detecting recovery problems just in time: application of automated linguistic analysis and supervised machine learning to an online substance abuse forum. Journal of Medical Internet Research, 20(6), e10136. https://doi.org/10.2196/10136.CrossRefPubMedPubMedCentral

Lee, Y. L., Tsung, P. K., & Wu, M. (2018). Technology trend of edge AI. In 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), (pp. 1–2). IEEE.

Lenhard, F., Sauer, S., Andersson, E., Månsson, K., Mataix-Cols, D., Rück, C., & Serlachius, E. (2018). Prediction of outcome in internet-delivered cognitive behavior therapy for pediatric obsessive-compulsive disorder: a machine learning approach. International Journal of Methods in Psychiatric Research, 27(1), 1–11.

Liddy, E.D. (2001). Natural language processing. In Encyclopedia of library and information science, 2nd edn, New York, NY. Marcel Decker, Inc.

MacLeod, H., Yang, S., Oakes, K., Connelly, K., & Natarajan, S. (2016). Identifying rare diseases from behavioural data: a machine learning approach. In Connected Health: Applications, Systems and Engineering Technologies Conference, (pp. 130–139).

Mettler, T., Sprenger, M., & Winter, R. (2017). Service robots in hospitals: new perspectives on niche evolution and technology affordances. European Journal of Information Systems, 26(5), 451–468.

Miller, D. D., & Brown, E. W. (2018). Artificial intelligence in medical practice: the question to the answer? The American Journal of Medicine, 131(2), 129–133.PubMed

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G., The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med, 6(7), e1000097. https://doi.org/10.1371/journal.pmed1000097.CrossRefPubMedPubMedCentral

Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–15.

Oh, J., Yun, K., Hwang, J., & Chae, J. (2017). Classification of suicide attempts through a machine learning algorithm based on multiple systemic psychiatric scales. Frontiers in Psychiatry, 8. https://doi.org/10.3389/fpsyt.2017.00192.

Pan, I., Nolan, L. B., Brown, R. R., Khan, R., van der Boor, P., Harris, D. G., & Ghani, R. (2017). Machine learning for social services: a study of prenatal case management in Illinois. American Journal of Public Health, 107(6), 938–944.PubMedPubMedCentral

Pan, Y., Liu, H., Metsch, L. R., & Feaster, D. J. (2017). Factors associated with HIV testing among participants from substance use disorder treatment programs in the US: a machine learning approach. AIDS and Behavior, 21(2), 534–546.PubMedPubMedCentral

Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2). https://doi.org/10.7189/jogh.08.020303.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Vanderplas, J. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Pouke, M., & Häkkilä, J. (2013). Elderly healthcare monitoring using an avatar-based 3D virtual environment. International Journal of Environmental Research and Public Health, 10, 7283–7298.PubMedPubMedCentral

Pu, X., Fan, K., Chen, X., Ji, L., & Zhou, Z. (2016). Facial expression recognition from image sequences using twofold random forest classifier. Neurocomputing, 168, 1173–1180.

Rizzo, A., Lange, B., Buckwalter, J. G., Forbell, E., Kim, J., Sagae, K., Williams, J., Difede, J., Rothbaum, B. O., Reger, G., Parsons, T., & Kenny, P. (2011). SimCoach: an intelligent virtual human system for providing healthcare information and support. International Journal on Disability and Human Development. Special Issue: Disability, Virtual Reality and Assistive Technologies, 10(4), 277–281.

Rudin, C., & Wagstaff, K. L. (2014). Machine learning for science and society. Machine Learning, 95, 1–9.

Russell, S. J., & Norvig, P. (2010). Artificial intelligence: a modern approach. 3rd eds, Upper Saddle River, New Jersey: Pearson Education, Inc.

Ryu, S., Lee, H., Lee, D. K., & Park, K. (2018). Use of a machine learning algorithm to predict individuals with suicide ideation in the general population. Psychiatry Investigation, 15(11), 1030. https://doi.org/10.30773/pi.2018.08.27.CrossRefPubMedPubMedCentral

Schwartz, I. M., York, P., Nowakowski-Sims, E., & Ramos-Hernandez, A. (2017). Predictive and prescriptive analytics, machine learning and child welfare risk assessment: the Broward County experience. Children and Youth Services Review, 81, 309–320.

Siegwart, R., Nourbakhsh, I. R., Scaramuzza, D., & Arkin, R. C. (2011). Introduction to autonomous mobile robots. Cambridge, MA: MIT press.

Song, J., Song, T. M., Seo, D. C., & Jin, J. H. (2016). Data mining of web-based documents on social networking sites that included suicide-related words among Korean adolescents. Journal of Adolescent Health, 59(6), 668–673.PubMed

Song, J., Song, T. M., & Lee, J. R. (2018). Stay alert: forecasting the risks of sexting in Korea using social big data. Computers in Human Behavior, 81, 294–302.

Stilgoe, J. (2018). Machine learning, social learning and the governance of self-driving cars. Social Studies of Science, 48(1), 25–56.PubMed

Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: a tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329.

Takahashi, Y., & Evans, L. T. (2018). An application of machine learning for predicting rearrests: significant predictors for juveniles. Race and Social Problems, 10(1), 42–52.

Tangherlini, T. R., Roychowdhury, V., Glenn, B., Crespi, C. M., Bandari, R., Wadia, A., … & Bastani, R. (2016). “Mommy Blogs” and the vaccination exemption narrative: results from a machine-learning approach for story aggregation on parenting social media sites. JMIR Public Health and Surveillance, 2(2). https://doi.org/10.2196/publichealth.6586.

Teague, S. J., & Shatte, A. B. (2018). Exploring the transition to fatherhood: feasibility study using social media and machine learning. JMIR Pediatrics and Parenting, 1(2), e12371. https://doi.org/10.2196/12371.CrossRefPubMedPubMedCentral

Walsh, C. G., Ribeiro, J.D. & Franklin, J.C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 1–12. https://doi.org/10.2196/10754.

Wall, D. P., Dally, R., Luyster, R., Jung, J., & DeLuca, T. F. (2012). Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS ONE, 7(8), e43855. https://doi.org/10.1371/journal.pone.0043855.CrossRefPubMedPubMedCentral

Wallert, J., Gustafson, E., Held, C., Madison, G., Norlund, F., von Essen, L., Olsson, E., & Martin, G. (2017). Predicting adherence to internet-delivered psychotherapy for symptoms of depression and anxiety after myocardial infarction: machine learning insights from the U-CARE heart randomized controlled trial. Journal of Medical Internet Research, 20(10), e10754. https://doi.org/10.2196/10754.CrossRef

Wang, S. H., Ding, Y., Zhao, W., Huang, Y. H., Perkins, R., Zou, W., & Chen, J. J. (2016). Text mining for identifying topics in the literatures about adolescent substance use and depression. BMC Public Health, 16(1), 279–287.PubMedPubMedCentral

Wang, W., Hernandez, I., Newman, D. A., He, J., & Bian, J. (2016). Twitter analysis: studying US weekly trends in work stress and emotion. Applied Psychology, 65(2), 355–378.

Wiechmann, P., Lora, K., Branscum, P., & Fu, J. (2017). Identifying discriminative attributes to gain insights regarding child obesity in hispanic preschoolers using machine learning techniques. In Proceedings IEEE ICTAI, (pp. 11–15). https://doi.org/10.1109/ICTAI.2017.00014.

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., … & Bouwman, J. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3–10. https://doi.org/10.1038/sdata.2016.18.

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.

Titel: Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences
Auteurs: Mihaela Robila
Stefan A. Robila
Publicatiedatum: 12-12-2019
Uitgeverij: Springer US
Gepubliceerd in: Journal of Child and Family Studies / Uitgave 10/2020
Print ISSN: 1062-1024
Elektronisch ISSN: 1573-2843
DOI: https://doi.org/10.1007/s10826-019-01689-x

Bohn Stafleu van Loghum

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Objectives

Methods

Results

Conclusions

Log in om toegang te krijgen

Andere artikelen Uitgave 10/2020

The Moral Emotions of Guilt and Shame in Children: Relationship with Parenting and Temperament

A Quasi-Experimental Study of the Respectful Approach on Early Parenting Competence and Stress

Racial and Ethnic Comparison of Ecological Risk Factors and Youth Outcomes: A Test of the Desensitization Hypothesis

Exploring the Reliability and Validity of the TechU-Q to Evaluate Device and Purpose Specific Screen Use in Preschool Children and Parents

A Closer Look at Sexual Passion in Relationships

Coming Out to Siblings and Parents in an Italian Sample of Lesbian Women and Gay Men