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7 results on '"Tolga Karaosman"'

1. Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

Author

Mirac Baris Usta, Koray Karabekiroglu, Berkan Sahin, Muazzez Aydin, Abdullah Bozkurt, Tolga Karaosman, Armagan Aral, Cansu Cobanoglu, Aysegül Duman Kurt, Neriman Kesim, İrem Sahin, and Emre Ürer

Subjects
autism, machine learning, prognosis, predictive measures, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

OBJECTIVE Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. METHOD The study the group comprised 433 children (mean age: 72.3 ± 45.9 months) with ASD diagnosis. The ASD symptoms were assessed by the Autism Behavior Checklist, Aberrant Behavior Checklist, Clinical Global Impression scales at baseline (T0) and 12th (T1), 24th (T2), and 36th (T3) months. We tested the performance of for machine learning algorithms (Naive Bayes, Generalized Linear Model, Logistic Regression, Decision Tree) on our data, including the 254 items in the baseline forms. Patients with ≤2 CGI points in ASD symptoms at in 36 months were accepted as the group who has “better outcome” as the prediction class. RESULTS The significant proportion of the cases showed significant improvement in ASD symptoms (39.7% in T1, 60.7% in T2; 77.8% in T3). Our machine learning model in T3 showed that diagnosis group affected the prognosis. In the autism group, older father and mother age; in PDD-NOS group, MR comorbidity, less birth weight and older age at diagnosis have a worse outcome. In Asperger’s Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. CONCLUSION In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD.

Published
2019
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2. Poster Presentations

Author

Arif Önder, Aslı Sürer Adanır, Öznur Bilaç, Canem Kavurma, Yakup Doğan, Şermin Yalın Sapmaz, Nurcan Bektaş Türkmen, Feyza Alyu Tekeş, Rana Arslan, Betül Kurtses Gürsoy, Büşra Öztürk, Yetiş Işıldar, Gülçin Bölük Uzunoğlu, Nuran Bilgen, Bilge Doğan, Ali Duman, Çağdaş Öykü Memiş, Oktay Kocabaş, Levent Sevinçok, Umut İrfan Üçel, Ümide Demir Özkay, Özgür Devrim Can, Kemal Akpınar, Osman İsmail Özdel, Sultan Taş Bora, Kadriye Buldukoğlu, Serdar Karatoprak, Yunus Emre Dönmez, Özlem Özcan, Hatice Altun, İdiris Altun, Ferhat Can Ardic, Samet Kose, Mustafa Solmaz, Filiz Kulacaoglu, Yasin Hasan Balcioglu, Yusuf Olğar, Belma Turan, Yusuf Öztürk, Volkan Gelegen, Lut Tamam, Şerife Aslan, Nevzat Çamlı, Osman Zülkif Topak, Selim Tümkaya, Figen Çulha Ateşci, Ozge Metin, Perihan Cam Ray, Gamze Yapca Kaypakli, Ezgi Eynalli, Aysegul Yolga Tahiroglu, Canan Kuygun Karci, Gonca Gul Celik, Alper Çimik, Feyza Alyu, Alper Zıblak, Ayşe Nur İnci Kenar, Nazlı Turan Yücel, Mustafa Burak Barbaros, Dilan Eker, Esen Çimen, İbrahim Gündoğmuş, Ayhan Algül, Abdulkadir Karagöz, Murat Kıyançiçek, Pelin Kalcı, Hakan Karaş, Aslıhan Polat, Zehra Alğan, Aslıhan Okan İbiloğlu, İbrahim İbiloglu, Dilek Aslaner, Mevlut Comlekci, Selim Sagir, Tuba Basoglu, Murat Eren Özen, Mehmet Hamdi Örüm, Aysun Kalenderoğlu, Gülümser Peltek, Salih Selek, Murad Atmaca, Mehmet Vural, Halef Aydın, Sedat Yılmaz, Ramazan İlyas Öner, Ayşe Sevgi Karadağ, Mahmut Zabit Kara, Miraç Barış Usta, Koray Karabekiroğlu, Berkan Şahin, Muazzez Aydın, Abdullah Bozkurt, Tolga Karaosman, Armağan Aral, Cansu Çobanoğlu, Ayşegül Duman Kurt, Neriman Kesim, İrem Şahin, Emre Ürer, Oğuzhan Bekir Eğilmez, Tijen Utkan, Tugce Demirtas Sahin, Semil Selcen Gocmez, Yusufhan Yazir, Feyza Aricioglu, Cangül Yurtdaş, Hacer Zortul, Bayram Yilmaz, Feyza Arıcıoğlu, Hatice Kübra Parlaktaş, Aylin Yaba Uçar, Bayram Yılmaz, Neslihan Kurtul, Asiye Arıcı, Ece Merve Yazar, Necati Uzun, Ömer Faruk Akça, İbrahim Kılınç, Tevfik Balcı, Ozge Bayru, Osman Bertizlioğlu, Funda Yiğit Yalçın, Abdullah Bolu, Hatice Akar, Mehmet Sinan Aydın, Ali Doruk, Tuğba Mutu, Esra Yazici, Derya Guzel, Atila Erol, Elvan Çiftçi, Heysem Kaya, Hüseyin Güleç, Albert Ali Salah, Tuğba Kalyoncu, Deniz Argüz Çıldır, Fazilet Ergüvendi, Ulkar Shamkhalova, Ezgi Eynallı Gok, Sakir Altunbasak, Seyda Besen Dogankoc, Nefise Hilal Cansever Ovetti, Özge Metin, and Ulkhar Shamkhalova

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Published
2018
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5. The Predictive Social and Emotional Measures in Toddlerhood for Psychiatric Morbidity in Adolescence

Author

Tolga Karaosman, İrem Şahin, Armagan Aral, Miraç Barış Usta, Koray Karabekiroglu, Ayşegül Duman Kurt, Muazzez Aydın, Abdullah Bozkurt, Berkan Şahin, Neriman Kesim, and OMÜ

Subjects
medicine.medical_specialty, Early childhood,assessment,follow-up, Health Care Sciences and Services, Follow-Up, Psychiatric Application, medicine, General Medicine, Sağlık Bilimleri ve Hizmetleri, Assessment, Psychiatry, Psychology, behavioral disciplines and activities, Early Childhood
Abstract

Objective: We aimed to determine the predictive factors for psychiatric problem severity and/or clinical approval 10 years after first assessment between ages 1-4 years. Method: The children that were assessed with the CBCL and the BITSEA in their 1-4 years of age were included. The follow-up assessments were made by telephone interviews 10 years later by child psychiatry residents. The primary caregivers responded the follow-up questionnaire items, such as social/academic/behavioral functioning and self-esteem and the items regarding the psychiatric approval, diagnosis, and treatment history. Results: The CBCL-Internalizing scores in toddlerhood were found to be significantly inversely correlated with social and behavioral functioning level and self-esteem scores. The paternal and maternal BITSEA-Competence scores were found to be positively correlated with social and academic functioning. The CBCL Withdrawn/Depressed points at first evaulation were determined to be statistically significantly predictive for psychiatric diagnosis. Conclusion: The study revealed that some measures including CBCL and BITSEA completed in toddlerhood significantly indicate the children under risk of psychiatric morbidity in adolescence. Therefore, we conclude that the widespread use of this assessment measures in toddlers and the referral of the group under risk to the psychiatry units for further investigations could be useful preventive interventions.

Published
2020

6. Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

Author

Berkan Şahin, Cansu Cobanoglu, Neriman Kesim, Abdullah Bozkurt, Tolga Karaosman, Koray Karabekiroglu, Emre Ürer, Ayşegül Duman Kurt, Muazzez Aydın, Miraç Barış Usta, Armagan Aral, İrem Şahin, and OMÜ

Subjects
Autism, autism, Neurosciences. Biological psychiatry. Neuropsychiatry, behavioral disciplines and activities, Outcome (game theory), 03 medical and health sciences, 0302 clinical medicine, predictive measures, mental disorders, medicine, 0501 psychology and cognitive sciences, Pharmacology (medical), business.industry, 05 social sciences, medicine.disease, Term (time), Psychiatry and Mental health, machine learning, prognosis, Core symptoms, business, 030217 neurology & neurosurgery, RC321-571, 050104 developmental & child psychology, Clinical psychology
Abstract

Usta, Mirac Baris/0000-0002-1573-3165; Sahin, Berkan/0000-0003-4699-3418 WOS: 000481832500012 OBJECTIVE Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. METHOD The study the group comprised 433 children (mean age: 72.3 +/- 45.9 months) with ASD diagnosis. The ASD symptoms were assessed by the Autism Behavior Checklist, Aberrant Behavior Checklist, Clinical Global Impression scales at baseline (T0) and 12th (T1), 24th (T2), and 36th (T3) months. We tested the performance of for machine learning algorithms (Naive Bayes, Generalized Linear Model, Logistic Regression, Decision Tree) on our data, including the 254 items in the baseline forms. Patients with

Published
2019

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