PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article A Machine Learning Model for Predicting Oligoclonal Band Positivity Using Routine Cerebrospinal Fluid and Serum Biochemical Markers(Oxford University Press Inc, 2025) Gözgöz, Hazar; Orhan, Oğuzhan; Akan Konuk, Başak; Akan, PınarOBJECTIVE: To develop and validate a machine learning model for predicting oligoclonal band (OCB) positivity using routine cerebrospinal fluid (CSF) and serum biochemical markers to improve the diagnostic accuracy and efficiency of assessing intrathecal immunoglobulin G (IgG) synthesis. METHODS: In this retrospective study (n = 1709), an ensemble model was developed using 8 refined CSF and serum parameters. Combining optimized CatBoost, XGBoost, and LightGBM classifiers, the model was trained and evaluated using a 2-phase workflow, including 5-fold cross-validation and validation on independent internal (n = 342) and external (n = 49) cohorts. RESULTS: The developed ensemble model achieved a receiver operating characteristic-area under the curve (ROC-AUC) of 0.902 on the internal test set, significantly outperforming the conventional IgG index (ROC-AUC, 0.795). At its optimal threshold, the model demonstrated an accuracy of 0.830, with a sensitivity of 0.714 and a specificity of 0.916. On the external validation cohort, it achieved 90% accuracy and 96% sensitivity. CONCLUSIONS: A novel machine learning ensemble model accurately predicts OCB positivity using routine laboratory data and demonstrates superior performance compared with the IgG index. This approach represents a significant step in applying artificial intelligence in laboratory medicine, with the potential to enhance diagnostic efficiency. Prospective, multicenter validation is essential for broader clinical implementation. © The Author(s) 2025.Article Assessment of Undergraduate Health Students' Perception and Satisfaction on Training and Participation in Community Health Outreach(Springer, 2023) Adegbore, Abidemi Kafayat; Adedokun, Amudatu Ambali; Adegoke, Juliet Ifeoluwa; Lawal, Maruf Ayobami; Oke, MuseAimThe need to improve training of health professionals has increased in recent years due to increasing frequencies of public health events. Consequently, a descriptive cross-sectional survey was carried out to determine the level of satisfaction and knowledge acquired by undergraduate students in the health sciences during a community health outreach program.Subject and methodsStudents were invited to complete an online-administered questionnaire (consisting of both open- and closed-ended questions) to assess their perceptions and experiences on the community health outreach program. Additionally, the survey was carried out to assess the quality of training provided and obtain suggestions for further improvements. Responses were collected and analysed using Microsoft Excel.ResultsMost respondents (>83%) reported satisfaction with the community diagnosis and community intervention briefing and training sessions. All respondents reported familiarity with standard community health outreach instruments and were capable of identifying environmental health risk factors that may contribute to the spread of communicable diseases. Interestingly, respondents reported greater appreciation of health challenges faced by rural communities. However, respondents expressed dissatisfaction with the duration of the outreach program (24%) and funding (15%).ConclusionAlthough respondents reported overall satisfaction with the organization and execution of the health outreach program, certain aspects of the program were deemed unsatisfactory. Despite the shortcomings, we believe that our student-centred learning strategy is readily adaptable for training future healthcare professionals and improving health literacy of rural communities, particularly in sub-Saharan Africa.