Yıl 2021, Cilt 13 , Sayı 2, Sayfalar 332 - 353 2021-06-30

Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı
Use of Machine Learning Methods in Psychiatry

İlkim Ecem EMRE [1] , Cumhur TAŞ [2] , Çiğdem EROL [3]


Yapay zeka ve veri analizinde gün geçtikçe daha popüler hale gelen makine öğrenmesi yöntemleri birçok farklı alanda veriden öğrenmeyi sağlamaktadır. Sağlık alanında yapılan çalışmalarda bu yöntemler sağlık çalışanlarına ve hekimlere destek sunmaktadır. Psikiyatri de bu alanlardan bir tanesidir. Hastalıkların tanı, hastalık seyrinin tahmini veya bir tedaviye verilecek yanıtın gözlemlenmesi gibi problemlere makine öğrenmesi yöntemleri destek sağlamaktadır. Bu çalışma kapsamında psikiyatri alanında yapılmış olan makine öğrenmesi çalışmaları incelenmiştir. Çalışmanın amacı, makine öğrenmesi yöntemlerinin psikiyatri alanında kullanımının araştırılmasıdır. Özellikle elektroensefalografi (EEG) verisi kullanılan araştırmalara odaklanılmıştır. Bu amaçla, psikiyatride alanında yapılan makine öğrenmesi ile ilgili olan SCOPUS ve Google Scholar kaynaklarındaki yayınlar incelenmiştir. Literatürdeki genel durumun ortaya konması amacıyla, psikiyatri alanında makine öğrenmesi yöntemlerinden yararlanan çalışmalara incelenmiştir. Sonrasında ise daha detaylı bir şekilde psikiyatri alanında makine öğrenmesi ve EEG verisi kullanılarak yapılan araştırmalar incelenmiştir. Bu çalışmanın psikiyatride makine öğrenmesi ile ilgili yapılan yayınlar ve özellikle EEG verisi kullanılan yayınların derlenmesi açısından araştırmacılara faydalı olabileceği umulmaktadır.
Machine learning methods, which are becoming more and more popular in artificial intelligence and data analysis, provide learning from data in many different fields. In the studies conducted in the field of health, these methods support healthcare professionals and physicians. Psychiatry is one of these areas. Machine learning methods provide support to problems such as diagnosis, prediction of disease course or monitoring response to a treatment. In this study, machine learning studies in the field of psychiatry are examined.The aim of the study is to examine the studies of machine learning in the field of psychiatry and especially the studies conducted using electroencephalography (EEG) data. Accordingly, studies on machine learning in the field of psychiatry in SCOPUS and Google Scholar sources were examined. In order to reveal the general situation in the literature, studies using machine learning methods in the field of psychiatry were examined. Afterwards, studies using both machine learning methods and EEG data in psychiatry were examined. It is hoped that this study will be useful to researchers in terms of the publications about machine learning in psychiatry and especially the publications using EEG data.
  • Agambayev S (2014) Analysis of electroencephalography (EEG) signals taken from patients suffer from major depressive disorder / Majör depresyonlu hastalardan alınan EEG sinyallerinin analizi. (Yayımlanmamış yüksek lisans tezi). Fatih Üniversitesi, İstanbul.
  • Ahmadlou M, Rostami R, Sadeghi V (2012) Which attention-deficit/hyperactivity disorder children will be improved through neurofeedback therapy? A graph theoretical approach to neocortex neuronal network of ADHD. Neuroscience Letters, 516(1):156-160.
  • Akdemir Akar S (2011) Analysis of physiological and electrophysiological parameters in patients with schizophrenia / Şizofreni hastalarında fizyolojik ve elektrofizyolojik parametrelerin analizi. (Yayımlanmamış doktora tezi). Fatih Üniversitesi, İstanbul.
  • Akgül Ö (2019) Şizofrenide motivasyonel bozukluğun elektrofizyolojik ve nöropsikolojik yöntemlerle incelenmesi: Kilo alımı ve sigara kullanımı ile ilişkisi / Investigation of motivational dysfunction and its relation to weight gain and smoking in schizophrenia with electrophysiological and neuropsychological methods. (Yayımlanmamış doktora tezi). Dokuz Eylül Üniversitesi, İzmir.
  • Aktemur Z (2015) EEG signal analysis in conversion disorder patients / Konversiyon bozukluğu hastalarında EEG sinyal analizi. (Yayımlanmamış yüksek lisans tezi). Fatih Üniversitesi, İstanbul.
  • Alchalabi AE (2017) A wearable EEG-based serious game for focus improvement and diagnosing ADHD/ADD patients by EEG signals classification / Odaklanmanın geliştirilmesi için giyilebilen EEG temelli uygulamalı oyun ve EEG sinyal sınıflandırması ile DEHB hastalarına tanı koyma. (Yayımlanmamış yüksek lisans tezi). İstanbul Şehir Üniversitesi, İstanbul.
  • Aldemir R (2019) Dikkat eksikliği ve hiperaktivite bozukluğu olan çocuklarda ilaçla tedavi sürecinin EEG analizleriyle değerlendirilmesi / Evaluation of drug treatment processes of children with attention deficit and hyperactivity by EEG analysis. (Yayımlanmamış doktora tezi). Erciyes Üniversitesi, Kayseri.
  • Al-Kaysi AM, Al-Ani A, Loo CK, Breakspear M, Boonstra TW (2016) Predicting brain stimulation treatment outcomes of depressed patients through the classification of EEG oscillations. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) içinde:5266-5269. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • Altınbaşak G (2019) Bipolar ve unipolar bozuklukların uygun biyobelirteç kullanarak makine öğrenme yöntemleri ile sınıflandırılması / Classification of bipolar and unipolar disorder using biomarkers by machine learning methods. (Yayımlanmamış yüksek lisans tezi). Üsküdar Üniversitesi, İstanbul.
  • Altuğlu TB, Metin B, Tülay EE, Tan O, Sayar GH, Taş C et al. (2020) Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker. Clinical Neurophysiology, 131(3):716-724.
  • Aria M, Cuccurullo C (2017) bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4):959-975.
  • Aydın M (2020) Depresyon hastalarında TMU tedavisine olumlu yanıt verenler ile olumlu yanıt vermeyenlerin qeeg verilerinin incelenmesi / The analysi̇s of qeeg data of depressi̇on pati̇ents who responded posi̇ti̇vely and di̇d not responded posi̇ti̇vely to tms treatment. (Yayımlanmamış yüksek lisans tezi). Üsküdar Üniversitesi, İstanbul.
  • Bailey NW, Hoy KE, Rogasch NC, Thomson RH, McQueen S, Elliot D et al. (2018) Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders. Brain Stimulation, 11(1):190-203.
  • Balaban ME, Kartal E (2018) Veri Madencilği ve Makine Öğrenmesi Temel Algoritmaları ve R Dili ile Uygulamaları (2. bs.). İstanbul: Çağlayan Kitapevi.
  • Barzilay R, Israel N, Krivoy A, Sagy R, Kamhi-Nesher S, Loebstein O et al. (2019) Predicting Affect Classification in Mental Status Examination Using Machine Learning Face Action Recognition System: A Pilot Study in Schizophrenia Patients. Frontiers in Psychiatry, 10:288.
  • Başaran M (2019) Dikkat eksikliği ve hiperaktivite bozukluğunun(DEHB) EEG sinyalleri kullanılarak yapay sinir ağları ile kestirimi / Estimation of attention deficit and hyperactivity disorder (ADHD) with artificial neural networks using EEG signals. (Yayımlanmamış yüksek lisans tezi). Dumlupınar Üniversitesi, Kütahya.
  • Bedi G, Carrillo F, Cecchi GA, Slezak DF, Sigman M, Mota NB et al. (2015) Automated analysis of free speech predicts psychosis onset in high-risk youths. Npj Schizophrenia, 1(1):1-7.
  • Bishop, C (2006). Pattern Recognition and Machine Learning. Information Science and Statistics. New York: Springer-Verlag.
  • Boyacı, P (2012). Hafif kognitif bozukluk hastaları ile sağlıklı yaşlılarda görsel olay ilişkili potansiyeller ve nöropsikolojik testlerin kesitsel olarak incelenmesi / Cross-sectional analysis of visual event related potential and its correlation with neuropsychological tests in mild cognitive impairment patients and healthy controls. (Yayımlanmamış yüksek lisans tezi). Dokuz Eylül Üniversitesi, İzmir.
  • Brodersen, KH, Deserno, L, Schlagenhauf, F, Zhihao, L, Penny, WD, Buhmann, JM ve Stephan, KE (2014). Dissecting psychiatric spectrum disorders by generative embedding—ScienceDirect. NeuroImage: Clinical, 4, 98-111.
  • Bzdok D, Meyer-Lindenberg A (2018) Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3):223-230.
  • Cao H, Meyer-Lindenberg A, Schwarz E (2018) Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry. International Journal of Molecular Sciences, 19(11):3387.
  • Çapkan Altun EN (2019) Opioid kullanım bozukluğu ve sağlıklı kontrol gruplarının sınıflandırılmasında QEEG tabanlı biyobelirteç ile makine öğrenme yöntemleri kullanılarak retrospektif olarak sınıflandırılması / QEEG-based biomarker for classification of opioid use disorder and control groups classification by using machine learning methods. (Yayımlanmamış yüksek lisans tezi). Üsküdar Üniversitesi, İstanbul.
  • De Almeida JRC, Phillips ML (2013) Distinguishing between unipolar depression and bipolar depression: Current and future clinical and neuroimaging perspectives. Biological Psychiatry, 73(2):111-118.
  • Dervent Özbek SÖ (2015) Dikkat eksikliği ve hiperaktivite bozukluğu olan erişkinlerde beynin dinlenim durumu içsel bağlantı ağlarının eş zamanlı EEG-fMRI ile araştırılması / Investigation of the resting-state intrinsic connectivity networks of the brain in adults with attention deficite hyperactivity disorder by using simultaneous EEG-fMRI during resting state measurements. (Yayımlanmamış doktora tezi). İstanbul Üniversitesi, İstanbul.
  • Dipnall J F, Pasco J A, Berk M, Williams LJ, Dodd S, Jacka FN et al. (2017) Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM). European Psychiatry: The Journal of the Association of European Psychiatrists, 39:40-50.
  • Dowd EC, Frank MJ, Collins A, Gold JM, Barch DM (2016) Probabilistic Reinforcement Learning in Patients With Schizophrenia: Relationships to Anhedonia and Avolition. Biological psychiatry: Cognitive neuroscience and neuroimaging, 1(5): 460-473. Dwyer DB, Falkai P, Koutsouleris N (2018) Machine Learning Approaches for Clinical Psychology and Psychiatry. Annual Review of Clinical Psychology, 14:91-118.
  • Edgcomb J, Shaddox T, Hellemann G, Brooks JO (2019) High-Risk Phenotypes of Early Psychiatric Readmission in Bipolar Disorder With Comorbid Medical Illness. Psychosomatics, 60(6):563-573.
  • Efron B, Tibshirani R (1993) An Introduction to the Bootstrap. New York: Chapman & Hall.
  • Ellis RJ, Wang Z, Genes N, Ma’ayan A (2019) Predicting opioid dependence from electronic health records with machine learning. BioData Mining, 12(1):3.
  • Eraldemi̇r SG, Kılıç Ü, Keleş MK, Demi̇rkol ME, Yıldırım E, Tamam L (2020) Classification of EEG Signals in Depressed Patients. Balkan Journal of Electrical and Computer Engineering, 8(1):103-107.
  • Ergüzel TT, Noyan CO, Eryılmaz G, Ünsalver BÖ, Cebi M, Tas C et al. (2019) Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures. Clinical EEG and Neuroscience, 50(5):303-310.
  • Ergüzel TT, Özekes S, Gültekin S, Tarhan N, Hızlı Sayar G, Bayram A (2015a) Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance. Psychiatry Investigation, 12(1):61-65.
  • Ergüzel TT, Özekes S, Sayar GH, Tan O, Tarhan N (2015b) A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder. Neurocomputing, 161:220-228.
  • Ergüzel TT, Taş C, Çebi M (2015c) A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders. Computers in Biology and Medicine, 64:127-137.
  • Ergüzel TT, Tarhan N (2018) Machine Learning Approaches to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Major Depressive Disorder. Y. Bi, S. Kapoor ve R. Bhatia (Ed.), Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 içinde. Lecture Notes in Networks and Systems, 16:391-401. Cham: Springer International Publishing.
  • Eugene A R, Masiak J, Eugene B (2018) Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning. F1000Research, 7:474.
  • Flach P (2012). Machine Learning: The Art and Science of Algorithms That Make Sense of Data. ABD: Cambridge University Press.
  • Fond G, Bulzacka E, Boucekine M, Schürhoff F, Berna F, Godin O et al. (2019) Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 92:8-18.
  • Fu CHY, Costafreda SG (2013) Neuroimaging-based biomarkers in psychiatry: Clinical opportunities of a paradigm shift. Canadian Journal of Psychiatry, 58(9), 499-508.
  • Galatzer-Levy IR, Karstoft, K-I, Statnikov A, Shalev AY (2014) Quantitative forecasting of PTSD from early trauma responses: A Machine Learning application. Journal of Psychiatric Research, 59:68-76.
  • Han D-H, Lee S, Seo DC (2020) Using machine learning to predict opioid misuse among U.S. adolescents. Preventive Medicine, 130, 105886.
  • Han J, Kamber M, Pei J (2012) Data mining: Concepts and techniques (3. bs.). ABD: Morgan Kaufman Publishers. Hatton CM, Paton L W, McMillan D, Cussens J, Gilbody S, Tiffin PA (2019) Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare. Journal of Affective Disorders, 246:857-860.
  • Hosseinifard B, Moradi MH, Rostami,R (2013) Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Computer Methods and Programs in Biomedicine, 109(3):339-345.
  • Huys QJM, Maia TV, Frank MJ (2016) Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3):404-413.
  • Iniesta R, Stahl D, McGuffin P (2016) Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine, 46(12):2455-2465.
  • Johannesen JK, Bi J, Jiang R, Kenney JG, Chen C-M A (2016) Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatric Electrophysiology, 2(1):3.
  • Just MA, Pan L, Cherkassky VL, McMakin DL, Cha C, Nock MK et al. (2017) Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nature Human Behaviour, 1(12):911-919.
  • Karakuş A (2019) Obsesif kompulsif bozukluk ve trikotilomani bozukluklarının uygun biyobelirteç kullanılarak makine öğrenme yöntemleri ile sınıflandırılması / Classification of obsessive-compulsive and trichotillomania disorders by using machine learning methods. (Yayımlanmamış yüksek lisans tezi). Üsküdar Üniversitesi, İstanbul.
  • Karamıkoğlu P (2015) Investigation of EEG signals of panic disorder patients during different auditory stimuli / Farklı işitsel uyaranlar sırasında panik bozukluğu hastalarının EEG sinyallerinin araştırılması. (Yayımlanmamış yüksek lisans tezi). Fatih Üniversitesi, İstanbul.
  • Karstoft K-I, Galatzer-Levy IR, Statnikov A, Li Z, Shalev AY, For members of the Jerusalem Trauma Outreach and Prevention Study (J-TOPS) group (2015) Bridging a translational gap: Using machine learning to improve the prediction of PTSD. BMC Psychiatry, 15:30.
  • Khodayari-Rostamabad A, Hasey GM, MacCrimmon DJ, Reilly JP, Bruin H de (2010a) A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clinical Neurophysiology, 121(12):1998-2006.
  • Khodayari-Rostamabad A, Reilly JP, Hasey G, de Bruin H, MacCrimmon D (2010b) Using pre-treatment EEG data to predict response to SSRI treatment for MDD. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology:6103-6106. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Arjantin.
  • Khodayari-Rostamabad A, Reilly JP, Hasey GM, de Bruin H, MacCrimmon D (2011) Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society içinde: 6418-6421. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, ABD: IEEE.
  • Khodayari-Rostamabad A, Reilly JP, Hasey GM, de Bruin H, Maccrimmon DJ (2013) A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 124(10):1975-1985.
  • Kısacık E (2018) Dikkat eksikliği ve hiperaktivite bozukluğu (DEHB) belirtileri gösteren ve göstermeyen sağlıklı erişkinlerde dikkat süreçlerinin EEG ile incelenmesi / The investigati̇on of attention processes on participants with and without attention deficit hyperactivity disorder symptoms: An EEG study. (Yayımlanmamış doktora tezi). Ankara Üniversitesi, Ankara.
  • Koçak OM (2010) Obsesif kompulsif bozukluğun iki hemisferli model ile açıklanması / Explanation of obsessive compulsive disorder using two hemisphere model. (Yayımlanmamış doktora tezi). Ankara Üniversitesi, Ankara.
  • Kohavi R (1995). A Study of Cross-Validation and Bootstrapfor Accuracy Estimation and Model Selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence:1137–1145. International Joint Conference on Artificial Intelligence.
  • Kuang D, He L (2014) Classification on ADHD with Deep Learning. 2014 International Conference on Cloud Computing and Big Data içinde: 27-32. 2014 International Conference on Cloud Computing and Big Data, Wuhan, China: IEEE.
  • Kutlu Y (2010) Multi-stage classification of abnormal patterns in EEG and e-ECG using model-free methods / Modelden bağımsız yöntemler kullanılarak EEG ve EKG içindeki anormal örüntülerin çok katlı sınıflandırılması. (Yayımlanmamış doktora tezi). Dokuz Eylül Üniversitesi, İzmir.
  • Lee D (2013) Decision Making: From Neuroscience to Psychiatry. Neuron, 78(2):233-248.
  • Li Y, Rosenfeld B, Pessin H, Breitbart W (2017) Bayesian Nonparametric Clustering of Patients with Advanced Cancer on Anxiety and Depression. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA):674-678. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Meksika: IEEE.
  • Markowetz A, Błaszkiewicz K, Montag C, Switala C, Schlaepfer TE (2014) Psycho-Informatics: Big Data shaping modern psychometrics. Medical Hypotheses, 82(4):405-411.
  • McGorry PD, Hartmann JA, Spooner R, Nelson B (2018) Beyond the “at risk mental state” concept: Transitioning to transdiagnostic psychiatry. World Psychiatry, 17(2): 133-142.
  • Mellem MS, Liu Y, Gonzalez H, Kollada M, Martin WJ, Ahammad P (2020) Machine Learning Models Identify Multimodal Measurements Highly Predictive of Transdiagnostic Symptom Severity for Mood, Anhedonia, and Anxiety. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(1):56-67.
  • Moberget T, Alnæs D, Kaufmann T, Doan NT, Córdova-Palomera A, Norbom LB et al. (2019) Cerebellar Gray Matter Volume Is Associated With Cognitive Function and Psychopathology in Adolescence. Biological Psychiatry, Clinical Impact of Psychosis Risk Mechanisms, 86(1): 65-75.
  • Mohammadi M, Al-Azab F, Raahemi B, Richards G, Jaworska N, Smith D et al. (2015) Data mining EEG signals in depression for their diagnostic value. BMC Medical Informatics and Decision Making, 15(1):108.
  • Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of Machine Learning. The MIT Press.
  • Mosteller F, Tukey J (1968) Data Analysis, Including Statistics. Lindzey G ve Aronson E (Ed.), Handbook of Social Psychology, vol. 2, 80-203. Addison Wesley.
  • Mueller A, Candrian G, Kropotov J D, Ponomarev V A, Baschera G-M (2010) Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomedical Physics, 4(S1).
  • Mumtaz W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS (2017a) Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomedical Signal Processing and Control, 31:108-115.
  • Mumtaz W, Xia L, Yasin MAM, Ali SSA, Malik AS (2017b) A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLOS ONE, 12(2):e0171409.
  • Mwangi B, Wu M-J, Cao B, Passos IC, Lavagnino L, Keser Z et al. (2016) Individualized Prediction and Clinical Staging of Bipolar Disorders Using Neuroanatomical Biomarkers. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(2):186-194.
  • Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis A, Vovk V, Vapnik V et al. (2011) Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage, 56(2):809-813.
  • Oquendo MA, Baca-Garcia E, Artés-Rodríguez A, Perez-Cruz F, Galfalvy H C, Blasco-Fontecilla H et al. (2012) Machine learning and data mining: Strategies for hypothesis generation. Molecular Psychiatry, 17(10):956-959.
  • Öztoprak H, Toycan M, Alp YK, Arıkan O, Doğutepe E, Karakaş S (2017) Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 128(12):2400-2410.
  • Papini S, Pisner D, Shumake J, Powers MB, Beevers CG, Rainey EE et al. (2018) Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization. Journal of Anxiety Disorders, 60:35-42.
  • Perez Arribas I, Goodwin G M, Geddes JR, Lyons T, Saunders KEA (2018) A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder. Translational Psychiatry, 8(1):274.
  • Perlis, R (2013) A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biological psychiatry, 74(1):7-14.
  • Perlis RH, Iosifescu DV, Castro VM, Murphy SN, Gainer VS, Minnier J et al. (2012) Using electronic medical records to enable large-scale studies in psychiatry: Treatment resistant depression as a model. Psychological Medicine, 42(1):41-50.
  • Pinaya WHL, Mechelli A, Sato JR (2019) Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study. Human Brain Mapping, 40(3).
  • Qin S, Young C B, Duan X, Chen T, Supekar K, Menon V (2014) Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood. Biological Psychiatry, 75(11):892-900.
  • Queirazza F, Fouragnan E, Steele J D, Cavanagh J, Philiastides M G (2019) Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression. Science Advances, 5(7):eaav4962.
  • Ramyead A, Studerus E, Kometer M, Uttinger M, Gschwandtner U, Fuhr P et al. (2016) Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients. The world journal of biological psychiatry: The official journal of the World Federation of Societies of Biological Psychiatry, 17(4):285-295.
  • Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Burger C et al. (2016) Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry, 73(6):557-564.
  • Ruşen E (2018). Erişkin tip DEHB’de sosyal kognisyon ve yürütücü işlevlerin ilişkisinin elektrofizyolojik yöntemle araştırılması / An electrophysiological research of social cognition and executive functions in adult ADHD. (Yayımlanmamış yüksek lisans tezi). İstanbul Medipol Üniversitesi, İstanbul.
  • Sato JR, Biazoli C E, Salum G A, Gadelha A, Crossley N, Vieira G et al. (2018) Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry, 19(2):119-129.
  • Schnack HG, Kahn RS (2016) Detecting neuroimaging biomarkers for psychiatric disorders: Sample size matters. Frontiers in Psychiatry, 7.
  • Sohn S, Kocher J.-P A, Chute C G, Savova G K (2011) Drug side effect extraction from clinical narratives of psychiatry and psychology patients. Journal of the American Medical Informatics Association, 18(SUPPL. 1):144-149.
  • Stamate D, Katrinecz A, Stahl D, Verhagen SJW, Delespaul PAEG, van Os J et al. (2019) Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches. Schizophrenia Research, 209:156-163.
  • Stephan KE, Mathys C (2014) Computational approaches to psychiatry. Current Opinion in Neurobiology, 25:85-92.
  • Stone M (1974) Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society. Series B (Methodological), 36(2):111–147.
  • Suhasini A, Palanivel S, Ramalingam V (2011) Multimodel decision support system for psychiatry problem. Expert Systems with Applications, 38(5):4990-4997.
  • Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A, Candrian G (2014) Machine learning approach for classification of ADHD adults. International Journal of Psychophysiology, Applied Neuroscience: Functional enhancement, prevention, characterisation and methodology. (Hosting the Society of Applied Neuroscience), 93(1):162-166.
  • Viviano JD, Buchanan R W, Calarco N, Gold JM, Foussias G, Bhagwat N et al. (2018) Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects. Biological Psychiatry, 84(9):665-674.
  • Walsh CG, Ribeiro JD, Franklin JC (2018) Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. Journal of Child Psychology and Psychiatry, 59(12):1261-1270.
  • Walss-Bass C, Suchting R, Olvera RL, Williamson DE (2018) Inflammatory Markers as Predictors of Depression and Anxiety in Adolescents: Statistical Model Building with Component-Wise Gradient Boosting. Journal of affective disorders, 234:276-281.
  • Whelan R, Garavan H (2014) When optimism hurts: Inflated predictions in psychiatric neuroimaging. Biological Psychiatry, 75(9):746-748.
  • Wiecki TV, Poland J, Frank MJ (2015) Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry: Clustering and Classification. Clinical Psychological Science, 3(3):378-399.
  • Yoon JH, Nguyen DV, McVay LM, Deramo P, Minzenberg MJ, Ragland JD et al. (2012) Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia. Schizophrenia Research, 135(1-3):28-33.
  • YÖK Tez Merkezi. (2020) Yükseköğretim Kurulu Başkanlığı Tez Merkezi. 16 Nisan 2020 tarihinde https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp adresinden erişildi.
  • Zhang X, Hu B, Zhou L, Moore P, Chen J. (2013) An EEG Based Pervasive Depression Detection for Females. Q. Zu, B. Hu ve A. Elçi (Ed.), Pervasive Computing and the Networked World içinde. Lecture Notes in Computer Science 7719:848-861. Joint International Conference on Pervasive Computing and the Networked World, Berlin, Heidelberg: Springer.
  • Zhao K ve So HC (2019) Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data. IEEE Journal of Biomedical and Health Informatics, 23(3):1304-1315. IEEE Journal of Biomedical and Health Informatics.
  • Zhao S, Zhao Q, Zhang X, Peng H, Yao Z, Shen J et al. (2017) Wearable EEG-Based Real-Time System for Depression Monitoring. Y. Zeng, Y. He, J. H. Kotaleski, M. Martone, B. Xu, H. Peng ve Q. Luo (Ed.), Brain Informatics içinde. Lecture Notes in Computer Science, 10654:190-201. Cham: Springer International Publishing.
  • Zilcha-Mano S, Roose SP, Brown PJ ve Rutherford BR. (2018) A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials. The American journal of geriatric psychiatry: Official journal of the American Association for Geriatric Psychiatry, 26(6): 669-677.
Birincil Dil tr
Konular Psikiyatri
Bölüm Derleme
Yazarlar

Orcid: 0000-0001-9507-8967
Yazar: İlkim Ecem EMRE (Sorumlu Yazar)
Kurum: MARMARA ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0002-4998-5272
Yazar: Cumhur TAŞ
Kurum: ÜSKÜDAR ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0002-5057-7145
Yazar: Çiğdem EROL
Kurum: İSTANBUL ÜNİVERSİTESİ
Ülke: Turkey


Teşekkür Bu çalışma İstanbul Üniversitesi Fen Bilimleri Enstitüsü Enformatik Anabilim Dalı’nda yürütülmekte olan doktora tez çalışmasından üretilmiştir.
Tarihler

Kabul Tarihi : 6 Kasım 2020
Yayımlanma Tarihi : 30 Haziran 2021

Bibtex @derleme { pgy779987, journal = {Psikiyatride Güncel Yaklaşımlar}, issn = {1309-0658}, eissn = {1309-0674}, address = {}, publisher = {Lut TAMAM}, year = {2021}, volume = {13}, pages = {332 - 353}, doi = {10.18863/pgy.779987}, title = {Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı}, key = {cite}, author = {Emre, İlkim Ecem and Taş, Cumhur and Erol, Çiğdem} }
APA Emre, İ , Taş, C , Erol, Ç . (2021). Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı . Psikiyatride Güncel Yaklaşımlar , 13 (2) , 332-353 . DOI: 10.18863/pgy.779987
MLA Emre, İ , Taş, C , Erol, Ç . "Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı" . Psikiyatride Güncel Yaklaşımlar 13 (2021 ): 332-353 <https://dergipark.org.tr/tr/pub/pgy/issue/58189/779987>
Chicago Emre, İ , Taş, C , Erol, Ç . "Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı". Psikiyatride Güncel Yaklaşımlar 13 (2021 ): 332-353
RIS TY - JOUR T1 - Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı AU - İlkim Ecem Emre , Cumhur Taş , Çiğdem Erol Y1 - 2021 PY - 2021 N1 - doi: 10.18863/pgy.779987 DO - 10.18863/pgy.779987 T2 - Psikiyatride Güncel Yaklaşımlar JF - Journal JO - JOR SP - 332 EP - 353 VL - 13 IS - 2 SN - 1309-0658-1309-0674 M3 - doi: 10.18863/pgy.779987 UR - https://doi.org/10.18863/pgy.779987 Y2 - 2020 ER -
EndNote %0 Psikiyatride Güncel Yaklaşımlar Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı %A İlkim Ecem Emre , Cumhur Taş , Çiğdem Erol %T Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı %D 2021 %J Psikiyatride Güncel Yaklaşımlar %P 1309-0658-1309-0674 %V 13 %N 2 %R doi: 10.18863/pgy.779987 %U 10.18863/pgy.779987
ISNAD Emre, İlkim Ecem , Taş, Cumhur , Erol, Çiğdem . "Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı". Psikiyatride Güncel Yaklaşımlar 13 / 2 (Haziran 2021): 332-353 . https://doi.org/10.18863/pgy.779987
AMA Emre İ , Taş C , Erol Ç . Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı. pgy. 2021; 13(2): 332-353.
Vancouver Emre İ , Taş C , Erol Ç . Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı. Psikiyatride Güncel Yaklaşımlar. 2021; 13(2): 332-353.
IEEE İ. Emre , C. Taş ve Ç. Erol , "Psikiyatride Makine Öğrenmesi Yöntemlerinin Kullanımı", Psikiyatride Güncel Yaklaşımlar, c. 13, sayı. 2, ss. 332-353, Haz. 2021, doi:10.18863/pgy.779987