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    EVALUATION OF SOCIO-DEMOGRAPHIC FACTORS AND COMORBIDITIES IN ADULT HEMOPHILIA PATIENTS
    (Dokuz Eylul Univ Inst Health Sciences, 2024) Karadag, Fatma Keklik; Demirci, Zuhal; Koseoglu, Fatos Dilan; Saydam, Guray; Sahin, Fahri
    Purpose: The association between socio-demographic factors and hemophilia status with the prevalence of comorbidities was evaluated. Material and Methods: Patients with hemophilia A (n=111) and B (n=24) who completed the questionnaire form about their socio-demographic factors were included in our study. Factor and inhibitor levels, comorbidities, factor replacement therapies, hemophilic arthropathy, viral status and annual bleeding episodes were recorded. Results: The median age was 39 years among 135 hemophilia patients, and 63.1% of all patients had severe hemophilia, which was significantly higher among hemophilia A (p=0.002). Most patients (74.8%) were treated with prophylactic factor replacement therapy. The inhibitor status was positive in 8.9% of all patients. The unemployment rate was found to be 33.3%. Annual bleeding episodes were higher in workers. Most patients (60%) had graduated from at least high school. Patients with severe hemophilia were significantly less educated than those with moderate to mild hemophilia (p=0.045). The prevalence of cardiovascular disease, hypertension, diabetes mellitus, and obesity was 6.7%; 17.8%, 13.3%, and 11.9% respectively. Although there was no association between obesity and annual bleeding episodes, right ankle was the most affected joint in overweight/obese patients. Conclusion: Age -related comorbidities and the relationship between hemophilia status and social life need further investigation.
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    JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels
    (Lippincott Williams & Wilkins, 2024) Koseoglu, Fatos Dilan; Karadag, Fatma Keklik; Bulbul, Hale; Alici, Erdem Ugur; Ozyilmaz, Berk; Ozdemir, Taha Resid
    The demand for Janus Kinase-2 (JAK2) testing has been disproportionate to the low yield of positive results, which highlights the need for more discerning test strategies. The aim of this study is to introduce an artificial intelligence application as a more rational approach for testing JAK2 mutations in cases of erythrocytosis. Test results were sourced from samples sent to a tertiary hospital's genetic laboratory between 2017 and 2023, meeting 2016 World Health Organization criteria for JAK2V617F mutation testing. The JAK2 Somatic Mutation Screening Kit was used for genetic testing. Machine learning models were trained and tested using Python programming language. Out of 458 cases, JAK2V617F mutation was identified in 13.3%. There were significant differences in complete blood count parameters between mutation carriers and non-carriers. Various models were trained with data, with the random forest (RF) model demonstrating superior precision, recall, F1-score, accuracy, and area under the receiver operating characteristic, all reaching 100%. Gradient boosting (GB) model also showed high scores. When compared with existing algorithms, the RF and GB models displayed superior performance. The RF and GB models outperformed other methods in accurately identifying and classifying erythrocytosis cases, offering potential reductions in unnecessary testing and costs.