Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

Authors

  • Archana Tandon United University Prayagraj
  • Bireshwar Dass Mazumdar Bennett University image/svg+xml
  • Manoj Kumar Pal United University Prayagraj

DOI:

https://doi.org/10.4108/eetpht.10.5541

Keywords:

Cognitive-based Neurological Diseases, Deep Learning, Natural Language Processing, Speech Recognition, Brain Imaging, Intelligent Computing Model

Abstract

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning.

OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases.

METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models.

RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children.

CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area.

Downloads

Download data is not yet available.

References

Pekny M, Pekna M, Messing A, Steinhäuser C, Lee JM, Parpura V, Hol EM, Sofroniew MV, Verkhratsky A. Astrocytes: a central element in neurological diseases. Acta neuropathologica. 2016; 31:323-345. DOI: https://doi.org/10.1007/s00401-015-1513-1

McEwen SE, Polatajko HJ, Huijbregts MP, Ryan JD. Exploring a cognitive-based treatment approach to improve motor-based skill performance in chronic stroke: Results of three single case experiments. Brain Injury. 2009; 23(13-14):1041-1053. DOI: https://doi.org/10.3109/02699050903421107

Chiu HL, Chu H, Tsai JC, Liu D, Chen YR, Yang HL, Chou KR. The effect of cognitive-based training for the healthy older people: A meta-analysis of randomized controlled trials. PloS one. 2017; 12(5):e0176742. DOI: https://doi.org/10.1371/journal.pone.0176742

Lim SH. Cognitive-based intervention for the older adults with mild cognitive impairment: a literature review. Journal of the Korea Convergence Society. 2021; 12(2):327-336.

Mohammed MA, Maashi MS, Arif M, Nallapaneni MK, Geman O. Intelligent systems and computational methods in medical and healthcare solutions with their challenges during COVID-19 pandemic. Journal of Intelligent Systems. 2021; 30(1):976-979. DOI: https://doi.org/10.1515/jisys-2021-0171

Chui KT, Lytras MD, Visvizi A, Sarirete A. An overview of artificial intelligence and big data analytics for smart healthcare: Requirements, applications, and challenges. Artificial intelligence and big data analytics for smart healthcare. 2021; 1:243-254. DOI: https://doi.org/10.1016/B978-0-12-822060-3.00015-2

Chen M, Herrera F, Hwang K. Cognitive computing: architecture, technologies and intelligent applications. Ieee Access. 2018; 6:19774-19783. DOI: https://doi.org/10.1109/ACCESS.2018.2791469

Tong Z, Ye F, Yan M, Liu H, Basodi S. A survey on algorithms for intelligent computing and smart city applications. Big Data Mining and Analytics. 2021; 4(3):155-172. DOI: https://doi.org/10.26599/BDMA.2020.9020029

Lott IT, Dierssen M. Cognitive deficits and associated neurological complications in individuals with Down's syndrome. The Lancet Neurology. 2010; 9(6):623-633. DOI: https://doi.org/10.1016/S1474-4422(10)70112-5

Sharma SR, Gonda X, Tarazi FI. Autism spectrum disorder: classification, diagnosis and therapy. Pharmacology & therapeutics. 2018; 190:91-104. DOI: https://doi.org/10.1016/j.pharmthera.2018.05.007

Polatajko HJ, Mandich AD, Miller LT, Macnab JJ. Cognitive orientation to daily occupational performance (CO-OP) part II the evidence. Physical & Occupational Therapy in Pediatrics. 2001; 20(2-3):83-106. DOI: https://doi.org/10.1080/J006v20n02_06

Green MF, Horan WP, Lee J. Nonsocial and social cognition in schizophrenia: current evidence and future directions. World psychiatry. 2019; 18(2):146-161.

Thapar A, Cooper M, Rutter M. Neurodevelopmental disorders. The Lancet Psychiatry. 2017; 4(4):339-346. DOI: https://doi.org/10.1016/S2215-0366(16)30376-5

Schneider A, Hagerman RJ, Hessl D. Fragile X syndrome—from genes to cognition. Developmental disabilities research reviews. 2009; 15(4):333-342. DOI: https://doi.org/10.1002/ddrr.80

Ghajar J. Traumatic brain injury. The Lancet. 2000; 356(9233):923-929. DOI: https://doi.org/10.1016/S0140-6736(00)02689-1

Sankar C, Mundkur N. Cerebral palsy-definition, classification, etiology and early diagnosis. The Indian Journal of Pediatrics. 2005; 72:865-868. DOI: https://doi.org/10.1007/BF02731117

Helmstaedter C, Witt JA. Epilepsy and cognition–a bidirectional relationship?. Seizure. 2017; 49:83-89. DOI: https://doi.org/10.1016/j.seizure.2017.02.017

Liu J, Cao L, Li H, Gao Y, Bu X, Liang K, Bao W, Zhang S, Qiu H, Li X, Hu X. Abnormal resting-state functional connectivity in patients with obsessive-compulsive disorder: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews. 2022; 135:104574. DOI: https://doi.org/10.1016/j.neubiorev.2022.104574

Koh JE, Ooi CP, Lim-Ashworth NS, Vicnesh J, Tor HT, Lih OS, Tan RS, Acharya UR, Fung DS. Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals. Computers in biology and medicine. 2022; 140:105120. DOI: https://doi.org/10.1016/j.compbiomed.2021.105120

Bitsko RH, Claussen AH, Lichstein J, Black LI, Jones SE, Danielson ML, Hoenig JM, Jack SP, Brody DJ, Gyawali S, Maenner MJ. Mental health surveillance among children—United States, 2013–2019. MMWR supplements. 2022; 71(2):1. DOI: https://doi.org/10.15585/mmwr.su7102a1

Williams White S, Keonig K, Scahill L. Social skills development in children with autism spectrum disorders: A review of the intervention research. Journal of autism and developmental disorders. 2007; 37:1858-68. DOI: https://doi.org/10.1007/s10803-006-0320-x

Mandich AD, Polatajko HJ, Rodger S. Rites of passage: Understanding participation of children with developmental coordination disorder. Human movement science. 2003; 22(4-5):583-595. DOI: https://doi.org/10.1016/j.humov.2003.09.011

Arora NK, Nair MK, Gulati S, Deshmukh V, Mohapatra A, Mishra D, Patel V, Pandey RM, Das BC, Divan G, Murthy GV. Neurodevelopmental disorders in children aged 2–9 years: Population-based burden estimates across five regions in India. PLoS medicine. 2018; 15(7):e1002615. DOI: https://doi.org/10.1371/journal.pmed.1002615

Wetherby AM, Watt N, Morgan L, Shumway S. Social communication profiles of children with autism spectrum disorders late in the second year of life. Journal of autism and developmental disorders. 2007; 37:960-975. DOI: https://doi.org/10.1007/s10803-006-0237-4

Green MF, Horan WP, Lee J. Nonsocial and social cognition in schizophrenia: current evidence and future directions. World psychiatry. 2019;18(2):146-161. DOI: https://doi.org/10.1002/wps.20624

Camara WJ, Nathan JS, Puente AE. Psychological test usage: Implications in professional psychology. Professional psychology: Research and practice. 2000; 31(2):141.

Gaillard WD, Chiron C, Helen Cross J, Simon Harvey A, Kuzniecky R, Hertz‐Pannier L, Gilbert Vezina L. Guidelines for imaging infants and children with recent‐onset epilepsy. Epilepsia. 2009; 50(9):2147-2153. DOI: https://doi.org/10.1111/j.1528-1167.2009.02075.x

Zablotsky B, Pringle BA, Colpe LJ, Kogan MD, Rice C, Blumberg SJ. Service and treatment use among children diagnosed with autism spectrum disorders. Journal of developmental and behavioral pediatrics: JDBP. 2015; 36(2):98. DOI: https://doi.org/10.1097/DBP.0000000000000127

Cramer SC, Sur M, Dobkin BH, O'Brien C, Sanger TD, Trojanowski JQ, Rumsey JM, Hicks R, Cameron J, Chen D, Chen WG. Harnessing neuroplasticity for clinical applications. Brain. 2011; 134(6):1591-1609. DOI: https://doi.org/10.1093/brain/awr039

Lin ZH, Liu Y, Xue NJ, Zheng R, Yan YQ, Wang ZX, Li YL, Ying CZ, Song Z, Tian J, Pu JL. Quercetin protects against MPP+/MPTP-induced dopaminergic neuron death in Parkinson’s disease by inhibiting ferroptosis. Oxidative Medicine and Cellular Longevity. 2022;2022.

Case-Smith J, Arbesman M. Evidence-based review of interventions for autism used in or of relevance to occupational therapy. The American Journal of Occupational Therapy. 2008 Jul 1;62(4):416-29. DOI: https://doi.org/10.5014/ajot.62.4.416

Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Information Fusion. 2019; 50:71-91.

Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database. 2020; 2020: baaa010.

Calhoun DA, Jones D, Textor S, Goff DC, Murphy TP, Toto RD, White A, Cushman WC, White W, Sica D, Ferdinand K. Resistant hypertension: diagnosis, evaluation, and treatment: a scientific statement from the American Heart Association Professional Education Committee of the Council for High Blood Pressure Research. Hypertension. 2008; 51(6):1403-1419. DOI: https://doi.org/10.1161/HYPERTENSIONAHA.108.189141

Langley P, Laird JE, Rogers S. Cognitive architectures: Research issues and challenges. Cognitive Systems Research. 2009; 10(2):141-160. DOI: https://doi.org/10.1016/j.cogsys.2006.07.004

Lin ZH, Liu Y, Xue NJ, Zheng R, Yan YQ, Wang ZX, Li YL, Ying CZ, Song Z, Tian J, Pu JL. Quercetin protects against MPP+/MPTP-induced dopaminergic neuron death in Parkinson’s disease by inhibiting ferroptosis. Oxidative Medicine and Cellular Longevity. 2022; 2022. DOI: https://doi.org/10.1155/2022/7769355

Hermessi H, Mourali O, Zagrouba E. Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain. Neural Computing and Applications. 2018; 30:2029-2045. DOI: https://doi.org/10.1007/s00521-018-3441-1

Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Information Fusion. 2019; 50:71-91. DOI: https://doi.org/10.1016/j.inffus.2018.09.012

Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine. 2013; 4(2):627.

Morgenstern JD, Rosella LC, Daley MJ, Goel V, Schünemann HJ, Piggott T. “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health. 2021; 21:1-4. DOI: https://doi.org/10.1186/s12889-020-10030-x

Sukhodolsky DG, Kassinove H, Gorman BS. Cognitive-behavioral therapy for anger in children and adolescents: A meta-analysis. Aggression and violent behavior. 2004; 9(3):247-269. DOI: https://doi.org/10.1016/j.avb.2003.08.005

Camara WJ, Nathan JS, Puente AE. Psychological test usage: Implications in professional psychology. Professional psychology: Research and practice. 2000; 31(2):141. DOI: https://doi.org/10.1037//0735-7028.31.2.141

Belanger HG, Vanderploeg RD, Curtiss G, Warden DL. Recent neuroimaging techniques in mild traumatic brain injury. The Journal of neuropsychiatry and clinical neurosciences. 2007; 19(1):5-20. DOI: https://doi.org/10.1176/jnp.2007.19.1.5

Wood JJ, Drahota A, Sze K, Har K, Chiu A, Langer DA. Cognitive behavioral therapy for anxiety in children with autism spectrum disorders: A randomized, controlled trial. Journal of Child Psychology and Psychiatry. 2009; 50(3):224-234. DOI: https://doi.org/10.1111/j.1469-7610.2008.01948.x

Brunzell T, Stokes H, Waters L. Trauma-informed positive education: Using positive psychology to strengthen vulnerable students. Contemporary School Psychology. 2016; 20:63-83. DOI: https://doi.org/10.1007/s40688-015-0070-x

Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neuroscience & Biobehavioral Reviews. 2012; 36(4):1140-52.

Omar KS, Mondal P, Khan NS, Rizvi MR, Islam MN. A machine learning approach to predict autism spectrum disorder; 07-09 February 2019; International conference on electrical, computer and communication engineering (ECCE): IEEE; 2019. (pp. 1-6). DOI: https://doi.org/10.1109/ECACE.2019.8679454

Verma VK, Verma S. Machine learning applications in healthcare sector: An overview. Materials Today: Proceedings. 2022; 57:2144-147. DOI: https://doi.org/10.1016/j.matpr.2021.12.101

Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM. Brain-computer interface technology: a review of the first international meeting. IEEE transactions on rehabilitation engineering. 2000; 8(2):164-73. DOI: https://doi.org/10.1109/TRE.2000.847807

Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support?. Journal of biomedical informatics. 2009; 42(5):760-72. DOI: https://doi.org/10.1016/j.jbi.2009.08.007

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017; 2(4). DOI: https://doi.org/10.1136/svn-2017-000101

Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. Ieee Access. 2017; 5:23626-23636.

Schmidt A, Wiegand M. A survey on hate speech detection using natural language processing. In Proceedings of the fifth international workshop on natural language processing for social media 2017; (pp. 1-10). DOI: https://doi.org/10.18653/v1/W17-1101

Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal. 2014; 5(4):1093-1113.

Lobo JM, Jiménez‐Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography. 2008; 17(2):145-151. DOI: https://doi.org/10.1111/j.1466-8238.2007.00358.x

Zhang D, Wang Y, Zhou L, Yuan H, Shen D, Alzheimer's Disease Neuroimaging Initiative. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage. 2011; 55(3):856-867. DOI: https://doi.org/10.1016/j.neuroimage.2011.01.008

Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neuroscience & Biobehavioral Reviews. 2012; 36(4):1140-1152. DOI: https://doi.org/10.1016/j.neubiorev.2012.01.004

Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal. 2014; 5(4):1093-1113. DOI: https://doi.org/10.1016/j.asej.2014.04.011

Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug discovery today. 2018; 23(6):1241-1250. DOI: https://doi.org/10.1016/j.drudis.2018.01.039

Nguyen M, He T, An L, Alexander DC, Feng J, Yeo BT, Alzheimer's Disease Neuroimaging Initiative. Predicting Alzheimer's disease progression using deep recurrent neural networks. NeuroImage. 2020; 222:117203. DOI: https://doi.org/10.1016/j.neuroimage.2020.117203

Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database. 2020; 2020:baaa010. DOI: https://doi.org/10.1093/database/baaa010

El-Dahshan ES, Hosny T, Salem AB. Hybrid intelligent techniques for MRI brain images classification. Digital signal processing. 2010; 20(2):433-41. DOI: https://doi.org/10.1016/j.dsp.2009.07.002

Miller N, Noble E, Jones D, Burn D. Life with communication changes in Parkinson’s disease. Age and ageing. 2006; 35(3):235-239. DOI: https://doi.org/10.1093/ageing/afj053

Michel CM, Murray MM. Towards the utilization of EEG as a brain imaging tool. Neuroimage. 2012; 61(2):371-385. DOI: https://doi.org/10.1016/j.neuroimage.2011.12.039

DeYoe EA, Bandettini P, Neitz J, Miller D, Winans P. Functional magnetic resonance imaging (FMRI) of the human brain. Journal of neuroscience methods. 1994; 54(2):171-187. DOI: https://doi.org/10.1016/0165-0270(94)90191-0

El Ayadi M, Kamel MS, Karray F. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern recognition. 2011; 44(3):572-587. DOI: https://doi.org/10.1016/j.patcog.2010.09.020

Britton JW, Frey LC, Hopp JL, Korb P, Koubeissi MZ, Lievens WE, Pestana-Knight EM, St Louis EK. Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants.

Jackson P, Moulinier I. Natural language processing for online applications: Text retrieval, extraction and categorization. John Benjamins Publishing; 2007. DOI: https://doi.org/10.1075/nlp.5

Lindquist MA, Loh JM, Atlas LY, Wager TD. Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling. Neuroimage. 2009; 45(1):S187-S198. DOI: https://doi.org/10.1016/j.neuroimage.2008.10.065

Roco MC, Bainbridge WS. Overview converging technologies for improving human performance: Nanotechnology, biotechnology, information technology, and cognitive science (NBIC). In Converging technologies for improving human performance: Nanotechnology, biotechnology, information technology and cognitive science 2003 (pp. 1-27). Dordrecht: Springer Netherlands. DOI: https://doi.org/10.1007/978-94-017-0359-8_1

McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ. Analysis of fMRI data by blind separation into independent spatial components. Human brain mapping. 1998; 6(3):160-188. DOI: https://doi.org/10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1

Calhoun VD, Adali T, Pearlson GD, Pekar JJ. Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms. Human brain mapping. 2001; 13(1):43-53. DOI: https://doi.org/10.1002/hbm.1024

Wahls S, Poor HV. Fast numerical nonlinear Fourier transforms. IEEE Transactions on Information Theory. 2015; 61(12):6957-6974. DOI: https://doi.org/10.1109/TIT.2015.2485944

Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clinical neurophysiology. 2005; 116(10):2266-2301. DOI: https://doi.org/10.1016/j.clinph.2005.06.011

Abbate A, Koay J, Frankel J, Schroeder SC, Das P. Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 1997; 44(1):14-26. DOI: https://doi.org/10.1109/58.585186

Sakkalis V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Computers in biology and medicine. 2011 Dec 1;41(12):1110-1117. DOI: https://doi.org/10.1016/j.compbiomed.2011.06.020

Stephan KE, Penny WD, Moran RJ, den Ouden HE, Daunizeau J, Friston KJ. Ten simple rules for dynamic causal modeling. Neuroimage. 2010 Feb 15;49(4):3099-109. DOI: https://doi.org/10.1016/j.neuroimage.2009.11.015

Dargan S, Kumar M, Ayyagari MR, Kumar G. A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering. 2020;27:1071-1092. DOI: https://doi.org/10.1007/s11831-019-09344-w

Stephan KE, Harrison LM, Kiebel SJ, David O, Penny WD, Friston KJ. Dynamic causal models of neural system dynamics: current state and future extensions. Journal of biosciences. 2007; 32:129-144. DOI: https://doi.org/10.1007/s12038-007-0012-5

Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical. 2018; 17:16-23. DOI: https://doi.org/10.1016/j.nicl.2017.08.017

Ng SH, Han S, Mao L, Lai JC. Dynamic bicultural brains: fMRI study of their flexible neural representation of self and significant others in response to culture primes. Asian Journal of Social Psychology. 2010; 13(2):83-91. DOI: https://doi.org/10.1111/j.1467-839X.2010.01303.x

Di Martino A, Zuo XN, Kelly C, Grzadzinski R, Mennes M, Schvarcz A, Rodman J, Lord C, Castellanos FX, Milham MP. Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder. Biological psychiatry. 2013; 74(8):623-32. DOI: https://doi.org/10.1016/j.biopsych.2013.02.011

Delorme A, Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods. 2004; 134(1):9-21. DOI: https://doi.org/10.1016/j.jneumeth.2003.10.009

Lindquist MA. The statistical analysis of fMRI data, statistical science. 2008; 23(4): 439-464. DOI: https://doi.org/10.1214/09-STS282

Narayanan S, Georgiou PG. Behavioral signal processing: Deriving human behavioral informatics from speech and language. Proceedings of the IEEE. 2013; 101(5):1203-1233. DOI: https://doi.org/10.1109/JPROC.2012.2236291

Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. Ieee Access. 2017; 5:23626-23636. DOI: https://doi.org/10.1109/ACCESS.2017.2762703

Billeci L, Narzisi A, Tonacci A, Sbriscia-Fioretti B, Serasini L, Fulceri F, Apicella F, Sicca F, Calderoni S, Muratori F. An integrated EEG and eye-tracking approach for the study of responding and initiating joint attention in Autism Spectrum Disorders. Scientific Reports. 2017; 7(1):13560. DOI: https://doi.org/10.1038/s41598-017-13053-4

Lance BJ, Kerick SE, Ries AJ, Oie KS, McDowell K. Brain–computer interface technologies in the coming decades. Proceedings of the IEEE. 2012; 100:1585-1599. DOI: https://doi.org/10.1109/JPROC.2012.2184830

Woo CW, Chang LJ, Lindquist MA, Wager TD. Building better biomarkers: brain models in translational neuroimaging. Nature neuroscience. 2017; 20(3):365-377. DOI: https://doi.org/10.1038/nn.4478

Musen MA, Middleton B, Greenes RA. Clinical decision-support systems. InBiomedical informatics: computer applications in health care and biomedicine 2021 (pp. 795-840). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-58721-5_24

Elder JH, Kreider CM, Brasher SN, Ansell M. Clinical impact of early diagnosis of autism on the prognosis and parent–child relationships. Psychology research and behavior management. 2017: 10:283-292. DOI: https://doi.org/10.2147/PRBM.S117499

Abouelmehdi K, Beni-Hessane A, Khaloufi H. Big healthcare data: preserving security and privacy. Journal of big data. 2018; 5(1):1-8. DOI: https://doi.org/10.1186/s40537-017-0110-7

Batista GE, Monard MC. An analysis of four missing data treatment methods for supervised learning. Applied artificial intelligence. 2003; 17(5-6):519-533. DOI: https://doi.org/10.1080/713827181

Batista GE, Monard MC. A study of K-nearest neighbour as an imputation method. Hybrid Intelligent Systems. 2002; 87:251-260.

Corbett-Davies S, Goel S. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv. 2018; 1808.00023.

Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence. 2019; 1(5):206-215. DOI: https://doi.org/10.1038/s42256-019-0048-x

Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019; 6(2):94-98. DOI: https://doi.org/10.7861/futurehosp.6-2-94

Isen AM. An influence of positive affect on decision making in complex situations: Theoretical issues with practical implications. Journal of consumer psychology. 2001; 11(2):75-85. DOI: https://doi.org/10.1207/153276601750408311

Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L. Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA) 2018. (pp. 80-89). IEEE. DOI: https://doi.org/10.1109/DSAA.2018.00018

Kuo, M. H. Opportunities and challenges of cloud computing to improve health care services. Journal of medical Internet research, 2011; 13(3), e1867. DOI: https://doi.org/10.2196/jmir.1867

Huang MX, Nichols S, Robb A, Angeles A, Drake A, Holland M, Asmussen S, D'Andrea J, Chun W, Levy M, Cui L. An automatic MEG low-frequency source imaging approach for detecting injuries in mild and moderate TBI patients with blast and non-blast causes. Neuroimage. 2012; 61(4):1067-1082. DOI: https://doi.org/10.1016/j.neuroimage.2012.04.029

Shih JJ, Krusienski DJ, Wolpaw JR. Brain-computer interfaces in medicine. Mayo clinic proceedings. 2012; 87(3): 268-279. DOI: https://doi.org/10.1016/j.mayocp.2011.12.008

Downloads

Published

25-03-2024

How to Cite

1.
Tandon A, Mazumdar BD, Pal MK. Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 25 [cited 2024 Apr. 21];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5541