Browsing by Author "Ozakbas, Serkan"
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Conference Object Characterizing Pain in Neuromyelitis Optica Spectrum Disorder: Prevalence and Clinical Features(Sage Publications Ltd, 2025) Yesiloglu, Pervin; Ozdogar, Asiye Tuba; Unal, Gozde Deniz; Engenc, Veysel; Zengin, Ela; Cilingir, Vedat; Ozakbas, SerkanConference Object Chronic Pain in Multiple Sclerosis: a Two-Year Longitudinal Study(Sage Publications Ltd, 2023) Karakas, Hilal; Ozdogar, Asiye Tuba; Ozcelik, Sinem; Kahraman, Turhan; Ozakbas, SerkanConference Object Clinical Characteristics and Disease Progression in Multiple Sclerosis: A Comparison of Late Middle-Age Onset Vs Late Adulthood Onset(Sage Publications Ltd, 2025) Ozakbas, Serkan; Simsek, Yasemin; Caliskan, Can; Ozdogar, Asiye TubaConference Object Clinical Comparison of Early and Late Onset Multiple Sclerosis at Age 35: Implications for Disease Progression and Management(Sage Publications Ltd, 2024) Kaya, Ergi; Aslan, Taha; Ozdogar, Asiye Tuba; Alizada, Said; Ozakbas, SerkanConference Object Cognitive Impairment in Multiple Sclerosis: Comparing Dual-Task Performance, Anxiety, Depression and Disability(Sage Publications Ltd, 2023) Sagici, Ozge; Ozdogar, Asiye Tuba; Baba, Cavid; Ozakbas, SerkanConference Object Cognitive Impairment Is Associated With Chronic Neuropathic Pain(Sage Publications Ltd, 2023) Karakas, Hilal; Ozdogar, Asiye Tuba; Sagici, Ozge; Samadzade, Ulvi; Kahraman, Turhan; Ozakbas, SerkanConference Object Cognitive Profiles of Relapsing Multiple Sclerosis Patients With Progression Independent of Relapse Activity Versus Non-NEDA Status(Sage Publications Ltd, 2025) Alizada, Said; Ozdogar, Asiye Tuba; Samadzade, Ulvi; Caliskan, Can; Ozakbas, SerkanArticle Comparative Analysis of Cognitive and Physical Characteristics in Late-Onset, Adult-Onset and Early-Onset Multiple Sclerosis Patients(Elsevier Sci Ltd, 2024) Ozakbas, Serkan; Kaya, Ergi; Aslan, Taha; Ozdogar, Asiye Tuba; Baba, CavidBackground: Late-onset multiple sclerosis (LOMS or L; MS) and early-onset MS (EOMS or E) are less common, and their prognosis can be different. To characterize the demographic and clinical features, and clinical outcomes of LOMS and EOMS patients, comparing them to adult-onset MS (AOMS or A) patients. Methods: The study was conducted as a secondary analysis of a prospective study. The participants were divided into three groups according to age of MS onset: early onset (<18 years of age), adult-onset (20-40 years of age), and late-onset (>55 years of age). Demographic variables, oligoclonal bands, IgG index, and Expanded Disability Status Scale (EDSS) score in admission, first year, second year and current EDSS were evaluated. The Timed 25- Foot Walk Test (T25FW), Timed Up and Go (TUG), Multiple Sclerosis Walking Scale-12, Single Leg Standing Test, Activity-Specific Balance Confidence Scale, Nine-Hole Peg Test, Epworth Sleepiness Scale and Restless Legs Syndrome Severity Scale were performed. Appropriate statistical analysis was made. Results: A total of 658 pwMS was included in the study and divided into three groups: EOMS (n n = 117), AOMS (n n = 499), and LOMS (n n = 42). Statistically significant differences were determined between groups in terms of age [L (mean:59.86+5.45 +5.45 years-y-)> A (36.87+9.12 +9.12 y)> E (26.56 +8.85 y), p < 0.001], education level, current EDSS score (L L > E, p < 0.001), EDSS score in first admission, EDSS score in the first year, EDSS score in the second year (L L > A > E, p < 0.001), reached an EDSS score 6 (E E > L p = 0.001, E > A p = 0.015), disease duration (E E > A, E > L , mean E = 11.66+9.7 +9.7 y, A = 7.99+7.4 +7.4 y, L = 6.31+4.67 +4.67 y) time switching second-line treatment to the third line (E E > L p < 0.001, A > L p = 0.002, mean E = 171.73+83.29 +83.29 months-m-, A = 136.13+65.75 +65.75 m, L = 65.85 +45.96 m), number of relapses (A A > E > L , median E = 4.0, A = 3.0, L = 2.0), distribution of MS type and oligoclonal band types. Significant differences were found in T25FW and TUG. Post-hoc analysis showed that participants in the LOMS group have longer T25FW (mean L = 7.8 + 6.11, A = 6.25+5.09, +5.09, E = 5.72+3.13, +3.13, p = 0.011) and TUG (mean L = 11.01+5.53, +5.53, A = 9.57+8.04, +8.04, E = 8.38+5.51, +5.51, p = 0.007) times than the AOMS and EOMS groups. Conclusion: Our result revealed that individuals with LOMS face elevated disability levels and a heightened propensity to transition from first-line treatments to more advanced therapeutic interventions. LOMS have worse lower extremity functional status than AOMS and EOMS patient. Clinical evaluations and treatment choices require more attention in LOMS. However, according to the low number of LOMS in our cohort, these results were considered cautious, and more wide and multi-center studies must be designed.Conference Object Comparative Analysis of Disease Progression and Disability Accumulation Between Early Onset and Adult Onset Multiple Sclerosis Patients at a Decade Post-Diagnosis(Sage Publications Ltd, 2024) Aslan, Taha; Kaya, Ergi; Ozdogar, Asiye Tuba; Yapici, Nurbanu; Ozakbas, SerkanConference Object Comparative Analysis of Restless Legs Syndrome and Neuropathic Pain Impact on Walking and Balance in Multiple Sclerosis: Clinical and Radiographic Insights(Sage Publications Ltd, 2024) Ozdogar, Asiye Tuba; Kaya, Ergi; Ozcelik, Sinem; Unal, Gozde Deniz; Ozakbas, SerkanConference Object Comparison of Early-Onset and Very Early-Onset People With Multiple Sclerosis Based on Cognitive and Physical Assessments(Sage Publications Ltd, 2023) Kaya, Ergi; Ozdogar, Asiye Tuba; Karakas, Hilal; Sagici, Ozge; Ozakbas, SerkanConference Object Comparison of the Objective and Subjective Cognitive Fatigue During Relapse and Remission in Individuals With Multiple Sclerosis: Preliminary Results(Sage Publications Ltd, 2024) Ozakbas, Serkan; Yigit, Pinar; Kara, Irem; Samadzade, Ulvi; Ozdogar, Asiye TubaConference Object Comparison of Two Commonly Used Depression Questionnaires Based on Reflecting Physical and Cognitive Functions in People With Multiple Sclerosis(Sage Publications Ltd, 2023) Ozdogar, Asiye Tuba; Sagici, Ozge; Aslan, Taha; Ozcelik, Sinem; Ozakbas, SerkanConference Object Correlation Between Lesion Loads and Cognitive, Social Cognitive, and Physical Measures in Persons With Multiple Sclerosis(Sage Publications Ltd, 2023) Aslan, Taha; Ozdogar, Asiye Tuba; Sagici, Ozge; Karakas, Hilal; Ozakbas, SerkanConference Object Determinants of Fatigue in People With Multiple Sclerosis: an Investigative Analysis(Sage Publications Ltd, 2024) Ozdogar, Asiye Tuba; Alizada, Said; Simsek, Yasemin; Ozakbas, SerkanConference Object Determinants of Postpartum Relapse in Multiple Sclerosis: a Comprehensive Analysis of Demographic and Clinical Factors(Sage Publications Ltd, 2024) Alizada, Said; Samadzade, Ulvi; Yapici, Nurbanu; Kaya, Ergi; Ozdogar, Asiye Tuba; Ozakbas, SerkanConference Object Development a Machine Learning Model To Prediction of Expanded Disability Status Scale in Multiple Sclerosis Patients(Sage Publications Ltd, 2024) Ozdogar, Asiye Tuba; Emec, Murat; Zengin, Ela; Ozcanhan, Mehmet Hilal; Ozakbas, SerkanArticle Development of a Machine Learning Model to Predict the Expanded Disability Status Scale in Multiple Sclerosis Patients(Elsevier Sci Ltd, 2026) Ozdogar, Asiye Tuba; Emec, Murat; Kaya, Ergi; Zengin, Ela Simay; Ozcanhan, Mehmet Hilal; Ozakbas, SerkanObjective: The assessment of disability in multiple sclerosis (MS) patients is crucial for treatment decisions and prognosis estimation. The Expanded Disability Status Scale (EDSS) provides a standardized way to quantify disability in MS. However, predicting EDSS scores can be challenging due to the complex and heterogeneous nature of the disease. Machine learning techniques offer a promising approach to predict EDSS scores based on various patient characteristics. Methods: 231 people with MS (pwMS) who had an assessment of physical, psychosocial, and cognitive functions in three timelines (baseline (T0), first year (T1), and second year (T2)) were enrolled. The dataset used for the study consists of 126 features. Feature selection was based on feature saliency and correlation analysis. Three machine learning models -XGBoost, Random Forest, and Linear Regression -were trained on the selected features. Hyperparameter tuning was also carried out on the models. Model performance was evaluated using standard evaluation metrics, including MAE, MSE, and R2. Results: The Machine Learning model based on the XGBoost algorithm performed best in predicting EDSS scores (T2). The MAE value obtained with the XGBoost model is 0.2361, the MSE value is 0.2408, and the R2 value is 0.9705. These results indicate that XGBoost's predictive ability on the current dataset is promising. Conclusion: Our study demonstrates the feasibility of using machine learning techniques to predict EDSS scores in MS patients. The developed models show promising performance and have the potential to enhance clinical decision-making and patient management in MS care.Article Development of Restless Legs Syndrome Severity Prediction Models for People with Multiple Sclerosis Using Machine Learning(Galenos Publ House, 2025) Kaya, Ergi; Emec, Murat; Ozdogar, Asiye Tuba; Zengin, Eta Simay; Karakas, Hitat; Dastan, Seda; Ozakbas, SerkanObjectives: This study aimed to develop an artificial intelligence-supported restless legs syndrome (RLS) severity prediction model for people with multiple sclerosis using machine learning methods. Patients and methods: Twenty-three individuals (14 females, 7 males; mean age: 40.6 +/- 10.9 years; range, 33 to 44 years) with multiple sclerosis with RLS were included in this observational study between March 2022 and March 2023. The International Restless Legs Syndrome Study Group Rating Scale was used to determine the RLS severity of the participants. The age, sex, body mass index, regular exercise habits, disease duration, Expanded Disability Status Scale (EDSS), estimated maximal aerobic capacity (VO2max), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, Multiple Sclerosis International Quality of Life Questionnaire, Multiple Sclerosis Walking Scale-12 (MSWS-12), and timed 25-foot walk test were determined as predictive variables. A correlation matrix was created. DecisionTree, RandomForest, and XGBoost machine learning methods were used to develop a model for predicting the RLS severity. Results: According to the obtained correlation matrix, PSQI scores strongly correlated with RLS severity (Pearson r=0.76). Meanwhile, EDSS scores (0.49), MSWS-12 scores (0.45), and disease duration (0.45) showed moderate correlations with RLS. Among the three different meachine learning methods, XGBoost demonstrated the best performance in predicting the severity of RLS, with a mean absolute error of 1.94, mean squared error of 4.58, mean absolute percentage error of 0.0735, and a test accuracy of 92.65%. The results showed that the severity of RLS could be estimated with 92.65% accuracy. Conclusion: This study showed a strong correlation between PSQI scores and RLS severity and that RLS severity could be predicted using machine learning methods.Conference Object Development of Restless Legs Syndrome Severity Prediction Models for People With Multiple Sclerosis Using Machine Learning(Sage Publications Ltd, 2023) Ozdogar, Asiye Tuba; Emec, Murat; Dastan, Seda; Karakas, Hilal; Baba, Cavid; Ozcanhan, Mehmet Hilal; Ozakbas, Serkan
