Use of machine learning for behavioral distinction of autism and ADHD
Open accessAbstract
Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today's pediatric population, the methods of diagnosis remain subjective, cumbersome and time intensive. With gaps upward of a year between initial suspicion and diagnosis, valuable time where treatments and behavioral interventions could be applied is lost as these disorders remain undetected. Methods to quickly and accurately assess risk for these, and other, developmental disorders are necessary to streamline the process of diagnosis and provide families access to much-needed therapies sooner. Using forward feature selection, as well as undersampling and 10-fold cross-validation, we trained and tested six machine learning models on complete 65-item Social Responsiveness Scale score sheets from 2925 individuals with either ASD (n=2775) or ADHD (n=150). We found that five of the 65 behaviors measured by this screening tool were sufficient to distinguish ASD from ADHD with high accuracy (area under the curve=0.965). These results support the hypotheses that (1) machine learning can be used to discern between autism and ADHD with high accuracy and (2) this distinction can be made using a small number of commonly measured behaviors. Our findings show promise for use as an electronically administered, caregiver-directed resource for preliminary risk evaluation and/or pre-clinical screening and triage that could help to speed the diagnosis of these disorders.
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Newer machine learning techniques may offer assistance by parsing out non-linear patterns in data that are missed with general linear model-based statistical tests and predictive modeling.
“Newer machine learning techniques may offer an assistance by parsing out non-linear patterns in data that are missed with general linear model-based statistical tests and predictive modeling (Duda et al., 2016; Uluyagmur-Ozturk et al., 2017; Vaidya et al., 2020).”
From Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD (2020) · cites this paper as Duda et al., 2016
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Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · see citing works
Lord, C., Rutter, M., & Le Couteur, A. (1994) · Journal of Autism and Developmental Disorders · doi:10.1007/bf02172145 · Cited by 9,065 · see citing works
Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., Kurzius-Spencer, M., Zahorodny, W., Robinson, C., Rosenberg, et al. (2018) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss6706a1 · Cited by 6,078 · see citing works
Naarden Braun, K. V., Lopez, M., White, T., et al. (2023) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/y3kk-5x30 · Cited by 1,381 · see citing works
The Autism Diagnostic Observation Schedule: Revised Algorithms for Improved Diagnostic Validity
Gotham, K., Risi, S., Pickles, A., & Lord, C. (2007) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-006-0280-1 · Cited by 1,161 · see citing works
Constantino, J. N. (2013) · Encyclopedia of Autism Spectrum Disorders · doi:10.1007/978-1-4419-1698-3_296 · Cited by 908 · see citing works
WIGGINS, L. D., BAIO, J., & RICE, C. (2006) · Journal of Developmental & Behavioral Pediatrics · doi:10.1097/00004703-200604002-00005 · Cited by 411 · see citing works
Use of machine learning to shorten observation-based screening and diagnosis of autism
Wall, D. P., Kosmicki, J. A., DeLuca, T. F., Harstad, E. B., & Fusaro, V. A. (2012) · Translational Psychiatry · doi:10.1038/tp.2012.10 · Cited by 286 · see citing works
Self-citationKosmicki, J. A., Sochat, V., Duda, M., & Wall, D. P. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.7 · Cited by 251 · see citing works
Self-citationUse of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism
Wall, D. P., Dally, R. L., Luyster, R., Jung, J.-Y., & DeLuca, T. F. (2012) · PLoS ONE · doi:10.1371/journal.pone.0043855 · Cited by 226 · see citing works
Self-citationMachine learning approach for classification of ADHD adults
Tenev, A., Markovska-Simoska, S., Kocarev, L., Pop-Jordanov, J., Müller, A. F., & Candrian, G. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.01.008 · Cited by 168 · see citing works
Psychopathology, Families, and Culture: Autism
Bernier, R., Mao, A. R., & Yen, J. (2010) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2010.07.005 · Cited by 167 · see citing works
Testing the accuracy of an observation-based classifier for rapid detection of autism risk
Duda, M., Kosmicki, J. A., & Wall, D. P. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.65 · Cited by 110 · see citing works
Self-citationPastor, P. N., Reuben, C. A., Duran, C. R., & Hawkins, L. D. (2015) · PubMed · Cited by 74 · see citing works
[Comorbidity of autism spectrum disorder and attention deficit with hyperactivity. A review study].
Berenguer, C., Miranda, A., Pastor-Cerezuela, G., & Rosello-Miranda, R. (2015) · PubMed · Cited by 30 · see citing works
Forner, C. B., Casas, A. M., Cerezuela, G. P., & Roselló, R. (2015) · Revista de Neurología · doi:10.33588/rn.60s01.2014559 · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning in autistic spectrum disorder behavioral research: A review and ways forward
Thabtah, F. (2018) · Informatics for Health and Social Care · doi:10.1080/17538157.2017.1399132 · Cited by 293 · see citing works
A new machine learning model based on induction of rules for autism detection
Thabtah, F., & Peebles, D. (2019) · Health Informatics Journal · doi:10.1177/1460458218824711 · Cited by 283 · see citing works
Artificial intelligence for precision medicine in neurodevelopmental disorders
Uddin, M. J., Wang, Y., & Woodbury‐Smith, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0191-0 · Cited by 257 · see citing works
Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review
Hyde, K. K., Novack, M. N., LaHaye, N., Parlett-Pelleriti, C., Anden, R., Dixon, D. R., & Linstead, E. J. (2019) · Review Journal of Autism and Developmental Disorders · doi:10.1007/s40489-019-00158-x · Cited by 243 · see citing works
Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders
Akter, T., Satu, M. S., Khan, M. I., Ali, M. H., Uddin, S., Lió, P., Quinn, J. M. W., & Moni, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2952609 · Cited by 239 · see citing works
Detection of Autism Spectrum Disorder in Children Using Machine Learning Techniques
Vakadkar, K., Purkayastha, D., & Krishnan, D. (2021) · SN Computer Science · doi:10.1007/s42979-021-00776-5 · Cited by 230 · see citing works
de Pablo, G. S., Studerus, E., Vaquerizo‐Serrano, J., Irving, J., Catalán, A., Oliver, D., Baldwin, H., Danese, A., Fazel, S., Steyerberg, E. W., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa120 · Cited by 221 · see citing works
A new computational intelligence approach to detect autistic features for autism screening
Thabtah, F., Kamalov, F., & Rajab, K. D. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.06.009 · Cited by 206 · see citing works
Autism Spectrum Disorder Screening
Thabtah, F. (2017) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3107514.3107515 · Cited by 193 · see citing works
A deep learning framework for identifying children with ADHD using an EEG-based brain network
Chen, H., Song, Y., & Xiaoli Li, A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.04.058 · Cited by 189 · see citing works
Rahman, M. M., Usman, O. L., Muniyandi, R. C., Sahran, S., Mohamed, S. B., & Razak, R. A. (2020) · Brain Sciences · doi:10.3390/brainsci10120949 · Cited by 178 · see citing works
Tai, A. M. Y., Albuquerque, A., Carmona, N. E., Subramanieapillai, M., Cha, D. S., Sheko, M., Lee, Y. L., Mansur, R. B., & McIntyre, R. S. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101704 · Cited by 168 · see citing works
Bashiri, A., Ghazisaeedi, M., & Shahmoradi, L. (2017) · Korean Journal of Pediatrics · doi:10.3345/kjp.2017.60.11.337 · Cited by 133 · see citing works
Early Autism Screening: A Comprehensive Review
Thabtah, F., & Peebles, D. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16183502 · Cited by 157 · see citing works
Moghaddari, M., Lighvan, . Z., & Danishvar, S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105738 · Cited by 149 · see citing works
A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders
Hasan, S. M. M., Uddin, M. P., Mamun, M. A., Sharif, M. I., Ulhaq, A., & Krishnamoorthy, G. (2022) · IEEE Access · doi:10.1109/access.2022.3232490 · Cited by 147 · see citing works
A machine learning autism classification based on logistic regression analysis
Thabtah, F., Abdelhamid, N., & Peebles, D. (2019) · Health Information Science and Systems · doi:10.1007/s13755-019-0073-5 · Cited by 145 · see citing works
Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms
Alsaade, F. W., & Al-zahrani, M. S. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8709145 · Cited by 142 · see citing works
RetractedAutism AI: a New Autism Screening System Based on Artificial Intelligence
Shahamiri, S. R., & Thabtah, F. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09743-3 · Cited by 138 · see citing works
Detecting autism spectrum disorder using machine learning techniques
Hossain, M. D., Kabir, M. A., Anwar, A., & Islam, M. Z. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00145-9 · Cited by 132 · see citing works
FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game
Alchalabi, A. E., Shirmohammadi, S., Eddin, A. N., & Elsharnouby, M. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2838158 · Cited by 126 · see citing works
Parikh, M. N., Li, H., & He, L. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00009 · Cited by 129 · see citing works
Crowdsourced validation of a machine-learning classification system for autism and ADHD
Duda, M., Haber, N., Daniels, J., & Wall, D. P. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.86 · Cited by 105 · see citing works
Self-citationTariq, Q., Fleming, S. L., Schwartz, J. N., Dunlap, K., Corbin, C. K., Washington, P. Y., Kalantarian, H., Khan, N. Z., Darmstadt, G. L., & Wall, D. P. (2019) · Journal of Medical Internet Research · doi:10.2196/13822 · Cited by 102 · see citing works
Self-citationAn XAI Based Autism Detection: The Context Behind the Detection
Biswas, M., Kaiser, M., Mahmud, M., Mamun, S. A., Hossain, M. S., & Rahman, M. A. (2021) · BI · doi:10.1007/978-3-030-86993-9_40 · Cited by 40 · see citing works
Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes
Ahmadi, A., Kashefi, M., Shahrokhi, H., & Nazari, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102227 · Cited by 87 · see citing works
Levy, S., Duda, M., Haber, N., & Wall, D. P. (2017) · Molecular Autism · doi:10.1186/s13229-017-0180-6 · Cited by 94 · see citing works
Self-citationKüpper, C. S., Stroth, S., Wolff, N., Hauck, F., Kliewer, N., Schad‐Hansjosten, T., Kamp‐Becker, I., Poustka, L., Roessner, V., Schultebraucks, K., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61607-w · Cited by 85 · see citing works
Song, D., Kim, S. Y., Bong, G., Kim, J. M., & Yoo, H. J. (2019) · Journal of korean Academy of Child and Adolescent Psychiatry · doi:10.5765/jkacap.190027 · Cited by 84 · see citing works
A Machine-Based Prediction Model of ADHD Using CPT Data
Slobodin, O., Yahav, I., & Berger, I. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.560021 · Cited by 83 · see citing works
Salman, O. H., Taha, Z. K., Alsabah, M. Q., Hussein, Y. S., Mohammed, A. S., & Aal-Nouman, M. I. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106357 · Cited by 81 · see citing works
Das, W., & Khanna, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-95673-5 · Cited by 74 · see citing works
An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism
Sewani, H., & Kashef, R. F. (2020) · Children · doi:10.3390/children7100182 · Cited by 71 · see citing works
Kim, W.-P., Kim, H.-J., Pack, S. P., Lim, J., Cho, C., & Lee, H. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.3502 · Cited by 70 · see citing works
Çavuş, N., Lawan, A. A., Ibrahim, Z., Dahiru, A., Tahir, S., Abdulrazak, U. I., & Hussaini, A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11040299 · Cited by 66 · see citing works
Autism Spectrum Disorder Prediction in Children Using Machine Learning
Abdelwahab, M. M., Al-Karawi, K. A., Hasanin, E. M., & Semary, H. E. (2024) · Journal of Disability Research · doi:10.57197/jdr-2023-0064 · Cited by 64 · see citing works
Annual Research Review: Translational machine learning for child and adolescent psychiatry
Dwyer, D., & Koutsouleris, N. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13545 · Cited by 65 · see citing works
Cao, M., Martin, E. L., & Li, X.-B. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02536-w · Cited by 67 · see citing works
ECNN: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder
Kashef, R. F. (2021) · Cognitive Systems Research · doi:10.1016/j.cogsys.2021.10.002 · Cited by 62 · see citing works
Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis
Maniruzzaman, M., Shin, J., & Hasan, M. A. M. (2022) · Applied Sciences · doi:10.3390/app12052737 · Cited by 62 · see citing works
A Machine Learning Way to Classify Autism Spectrum Disorder
Sujatha, R., Lakshminarayanan, A. S., Chatterjee, J. M., Alaboudi, A. A., & Jhanjhi, N. Z. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i06.19559 · Cited by 63 · see citing works
Early Detection of Autism by Extracting Features: A Case Study in Bangladesh
Satu, M. S., Sathi, F. F., Arifen, M. S., Ali, M. H., & Moni, M. A. (2019) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest.2019.8644357 · Cited by 58 · see citing works
Cordova, M., Shada, K., Demeter, D. V., Doyle, O., Miranda-Dominguez, O., Perrone, A., Schifsky, E., Graham, A., Fombonne, E., Langhorst, B., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102245 · Cited by 54 · see citing works
Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder
Goel, N., Grover, B., Anuj, A., Gupta, D. K., Khanna, A. K., & Sharma, M. (2020) · Physical Communication · doi:10.1016/j.phycom.2020.101115 · Cited by 56 · see citing works
Washington, P. Y., Kline, A., Mutlu, O. C., Leblanc, É., Hou, C., Stockham, N., Paskov, K. M., Chrisman, B., & Wall, D. P. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451701 · Cited by 54 · see citing works
Self-citationRadhakrishnan, M., Karthik, R., Choudhury, K., Won, D., & Manoharan, T. A. (2021) · Traitement du signal · doi:10.18280/ts.380332 · Cited by 57 · see citing works
Machine Learning to Study Social Interaction Difficulties in ASD
Georgescu, A. L., Koehler, J. C., Weiske, J., Vogeley, K. T., Koutsouleris, N., & Falter‐Wagner, C. M. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00132 · Cited by 53 · see citing works
Investigating autism etiology and heterogeneity by decision tree algorithm
Hassan, M. M., & Mokhtar, H. M. O. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100215 · Cited by 52 · see citing works
Evaluation on Machine Learning Algorithms for Classification of Autism Spectrum Disorder (ASD)
Abdullah, A. A., Rijal, S., & Dash, S. R. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1372/1/012052 · Cited by 51 · see citing works
Tartarisco, G., Cicceri, G., Pietro, D. D., Aiello, S., Leonardi, E., Marino, F., Chiarotti, F., Gagliano, A., Arduino, G. M., Apicella, F., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11030574 · Cited by 50 · see citing works
The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study
Lakkapragada, A., Kline, A., Mutlu, O. C., Paskov, K. M., Chrisman, B., Stockham, N., Washington, P. Y., & Wall, D. P. (2022) · JMIR Biomedical Engineering · doi:10.2196/33771 · Cited by 52 · see citing works
Self-citationMachine learning Classification of Dyslexic Children based on EEG Local Network FeaturesPreprint
Rezvani, Z., Zare, M., Žarić, G., Bonte, M. L., Tijms, J., van der Molen, M. W., & González, G. F. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/569996 · Cited by 47 · see citing works
Leblanc, É., Washington, P. Y., Varma, M., Dunlap, K., Penev, Y. P., Kline, A., & Wall, D. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-76874-w · Cited by 45 · see citing works
Self-citationImproved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence
Washington, P. Y., Kalantarian, H., Kent, J., Husic, A., Kline, A., Leblanc, É., Hou, C., Mutlu, O. C., Dunlap, K., Penev, Y. P., et al. (2020) · JMIR Pediatrics and Parenting · doi:10.2196/26760 · Cited by 43 · see citing works
Self-citationJoudar, S. S., Albahri, A. S., & Hamid, R. A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101131 · Cited by 43 · see citing works
Machine Learning Classifiers for Autism Spectrum Disorder: A Review
Eman, D., & Emanuel, A. W. R. (2019) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee48480.2019.9003807 · Cited by 41 · see citing works
Applying Machine Learning to Identify Autism With Restricted Kinematic Features
Zhao, Z., Zhang, X., Li, W., Hu, X., Qu, X., Cao, X., Liu, Y., & Lu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2950030 · Cited by 41 · see citing works
A predictive model for paediatric autism screening
Wingfield, B., Miller, S. M., Yogarajah, P., Kerr, D., Gardiner, B., Seneviratne, S., Samarasinghe, P. D., & Coleman, S. (2020) · Health Informatics Journal · doi:10.1177/1460458219887823 · Cited by 41 · see citing works
Classification of adult autistic spectrum disorder using machine learning approach
Mashudi, N. A., Ahmad, N. B., & Noor, N. M. (2021) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v10.i3.pp743-751 · Cited by 40 · see citing works
Derbali, M., Jarrah, M., & Randhawa, P. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140112 · Cited by 37 · see citing works
Autism spectrum Disorder detection Using Face Features based on Deep Neural network
Rashid, A., & Shaker, S. H. (2023) · Wasit Journal of Computer and Mathematics Science · doi:10.31185/wjcm.100 · Cited by 37 · see citing works
Feature Selection and Dimension Reduction of Social Autism Data
Washington, P. Y., Paskov, K. M., Kalantarian, H., Stockham, N., Voss, C., Kline, A., Patnaik, R., Chrisman, B., Varma, M., Tariq, Q., et al. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0062 · Cited by 37 · see citing works
Self-citationSchulte‐Rüther, M., Kulvičius, T., Stroth, S., Wolff, N., Roessner, V., Marschik, P. B., Kamp‐Becker, I., & Poustka, L. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13650 · Cited by 36 · see citing works
Mengi, M., & Malhotra, D. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09682-8 · Cited by 35 · see citing works
EASDM: Explainable Autism Spectrum Disorder Model Based on Deep Learning
Atlam, E.-S., Masud, M., Rokaya, M., Meshref, H., Gad, I., & Almars, A. M. (2024) · Journal of Disability Research · doi:10.57197/jdr-2024-0003 · Cited by 33 · see citing works
Autism Screening Using Deep Embedding Representation
Wang, H., Li, L., Chi, L., & Zhao, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22741-8_12 · Cited by 33 · see citing works
He, Q., Wang, Q., Wu, Y., Yi, L., & Wei, K. (2021) · PsyCh Journal · doi:10.1002/pchj.447 · Cited by 31 · see citing works
Haque, U. M., Kabir, E., & Khanam, R. (2023) · Health Information Science and Systems · doi:10.1007/s13755-023-00232-z · Cited by 30 · see citing works
Implementing machine learning in bipolar diagnosis in China
Ma, Y., Ji, J., Huang, Y., Gao, H., Li, Z., Dong, W., Zhou, S., Zhu, Y., Dang, W., Zhou, T., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0638-8 · Cited by 29 · see citing works
Sekaran, K., & Sudha, M. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02155-z · Cited by 28 · see citing works
RetractedIndividualized prediction models in ADHD: a systematic review and meta-regression
de Pablo, G. S., Iniesta, R., Bellato, A., Caye, A., Dobrosavljevic, M., Parlatini, V., Garcia‐Argibay, M., Li, L., Cabras, A., Ali, M. H., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02606-5 · Cited by 27 · see citing works
Souza, P. V. D. C., & Guimarães, A. J. (2018) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2018.8538736 · Cited by 27 · see citing works
Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach
Haque, M. M., Rabbani, M., Dipal, D. D., Zarif, M., Iqbal, A., Schwichtenberg, A., Bansal, N., Soron, T., Ahmed, S. I., & Ahamed, S. (2021) · JMIR Medical Informatics · doi:10.2196/29242 · Cited by 24 · see citing works
Song, C., Jiang, Z., Liu, D., & Wu, L. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.960672 · Cited by 28 · see citing works
MVPAlab: A machine learning decoding toolbox for multidimensional electroencephalography data
López-García, D., Peñalver, J. M. G., Górriz, J. M., & Ruz, M. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106549 · Cited by 25 · see citing works
Briguglio, M., Turriziani, L., Currò, A., Gagliano, A., Rosa, G. D., Caccamo, D., Tonacci, A., & Gangemi, S. (2023) · Brain Sciences · doi:10.3390/brainsci13060883 · Cited by 25 · see citing works
Liu, G., Xu, Y., Wang, X., Zhuang, X., Liang, H., Xi, Y., Lin, F., Pan, L., Zeng, T., Li, H., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16521-z · Cited by 24 · see citing works
Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection
Saranya, A., & Anandan, R. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023445 · Cited by 23 · see citing works
Rabbi, M. F., Zohra, F. T., Hossain, F., Akhi, N. N., Khan, S., Mahbub, M. K., & Biswas, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23599-3_14 · Cited by 23 · see citing works
RetractedFeasibility of detecting ADHD patients' attention levels by classifying their EEG signals
Alchalabi, A. E., Elsharnouby, M., Shirmohammadi, S., & Eddin, A. N. (2017) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2017.7985895 · Cited by 23 · see citing works
Mahmood, M. A., Jamel, L., Alturki, N. M., & Tawfeek, M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-96014-6 · Cited by 22 · see citing works
RetractedMachine Learning-Based Classification of Autism Spectrum Disorder across Age Groups
Resmi, K. R., Venkidusamy, P. N. P., Raju, G., George, B., & Thomas, N. (2024) · doi:10.3390/engproc2024062012 · Cited by 22 · see citing works
PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES
Demi̇rhan, A. (2018) · Mugla Journal of Science and Technology · doi:10.22531/muglajsci.422546 · Cited by 22 · see citing works
Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm BehaviourPreprint
Li, K., Javer, A., Keaveny, E. E., & Brown, A. E. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/222208 · Cited by 22 · see citing works
Lee, J., Ha, S., Ahmed, O., Cho, I., Lee, D., Kim, K., Lee, S., Kang, S., Suh, S. A., Chung, S., et al. (2022) · Sleep Medicine · doi:10.1016/j.sleep.2022.06.005 · Cited by 21 · see citing works
Autism screening: an unsupervised machine learning approach
Thabtah, F., Spencer, R., Abdelhamid, N., Kamalov, F., Wentzel, C., Ye, Y., & Dayara, T. (2022) · Health Information Science and Systems · doi:10.1007/s13755-022-00191-x · Cited by 21 · see citing works
Machine learning models using mobile game play accurately classify children with autism
Deveau, N. D., Washington, P. Y., Leblanc, É., Husic, A., Dunlap, K., Penev, Y. P., Kline, A., Mutlu, O. C., & Wall, D. P. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100057 · Cited by 20 · see citing works
Self-citationHan, Y., Rizzo, D. M., Hanley, J. P., Coderre, E. L., & Prelock, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269773 · Cited by 19 · see citing works
Autism Spectrum Disorder Prediction Using Machine Learning Algorithms
Selvaraj, S., Palanisamy, P., Parveen, H. S., & Monisha (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-37218-7_56 · Cited by 19 · see citing works
Avasthi, S., Sanwal, T., Sareen, P., & Tripathi, S. L. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-8786-7.ch005 · Cited by 18 · see citing works
A bibliometric analysis of studies on artificial intelligence in neuroscience
Tekin, U., & Dener, M. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1474484 · Cited by 18 · see citing works
Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels
Washington, P. Y., Kalantarian, H., Kent, J., Husic, A., Kline, A., Leblanc, É., Hou, C., Mutlu, O. C., Dunlap, K., Penev, Y. P., et al. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09936-4 · Cited by 18 · see citing works
Self-citationLella, E., & Vessio, G. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.06.001 · Cited by 17 · see citing works
Early Detection of Autism Spectrum Disorder (ASD) using Traditional Machine Learning Models
Mukherjee, P. K., Sadhukhan, S., Godse, M., & Chakraborty, B. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140626 · Cited by 17 · see citing works
GenAI, Robots, and Inclusive Special Education
Sağdıç, A., Elumar-efe, E., & Sani-Bozkurt, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1351-0.ch015 · Cited by 16 · see citing works
Intelligent Gaze-Based Screening System for Autism
Dris, A. B., Al‐Salman, A. S., Al‐Wabil, A., & Aldosari, M. (2019) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/cais.2019.8769452 · Cited by 16 · see citing works
A Review of Supportive Computational Approaches for Neurological Disorder Identification
Meedeniya, D., & Rubasinghe, I. D. (2020) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-7998-3069-6.ch016 · Cited by 16 · see citing works
Syriopoulou–Delli, C. K. (2025) · Electronics · doi:10.3390/electronics14050951 · Cited by 15 · see citing works
Robust diagnostic classification via Q-learning
Ardulov, V., Martínez, V. R., Somandepalli, K., Zheng, S., Salzman, E., Lord, C., Bishop, S. L., & Narayanan, S. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-90000-4 · Cited by 15 · see citing works
Efficacy of novel Summation-based Synergetic Artificial Neural Network in ADHD diagnosis
Peng, J., Debnath, M., & Biswas, A. K. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100120 · Cited by 15 · see citing works
- Authors
- Duda, M., Ma, R., Haber, N., & Wall, D. P.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Duda, M
- Wall, D P
- Venue
- Translational Psychiatry
- Published
- 2016-02-09
- Volume
- 6
- Issue
- 2
- Pages
- e732-e732
- ISSN
- 2158-3188 (ISSN-L)
- PMC
- PMC4872425
- OpenAlex
- W2263602591
- Cited by
- 300
- References
- 18
- Citations by year
- 2026: 35 · 2025: 43 · 2024: 34 · 2023: 42 · 2022: 40 · 2021: 38 · 2020: 26 · 2019: 24 · 2018: 8 · 2017: 10
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Translational Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Simons Foundation (240253)
- Hartwell Foundation
- Simons Foundation Autism Research Initiative
- National Institutes of Health
- National Institute of Mental Health (R01MH090611)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD091500)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Keywords
- autism spectrum disorder, attention deficit hyperactivity disorder, differential diagnosis, behavioral screening, Social Responsiveness Scale, machine learning classification, developmental disorders, diagnostic triage
- MeSH terms
- Machine Learning, Attention Deficit Disorder with Hyperactivity, Autistic Disorder, Child, Child Behavior, Diagnosis, Differential, Humans
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