Browsing by Author "Gulsen, Murat"

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    AI-ASSISTED Emotion Analysis During Complementary Feeding in Infants Aged 6-11 Months
    (2023) Gulsen, Murat; Aydin, Beril; Gurer, Guliz; Yalcin, Siddika Songul; 37742418
    This study aims to explore AI-assisted emotion assessment in infants aged 6-11 months during complementary feeding using OpenFace to analyze the Actions Units (AUs) within the Facial Action Coding system. When infants (n = 98) were exposed to a diverse range of food groups; meat, cow-milk, vegetable, grain, and dessert products, favorite, and disliked food, then video recordings were analyzed for emotional responses to these food groups, including surprise, sadness, happiness, fear, anger, and disgust. Time-averaged filtering was performed for the intensity of AUs. Facial expression to different food groups were compared with neutral states by Wilcoxon Singed test. The majority of the food groups did not significantly differ from the neutral emotional state. Infants exhibited high disgust responses to meat and anger reactions to yogurt compared to neutral. Emotional responses also varied between breastfed and non-breastfed infants. Breastfed infants showed heightened negative emotions, including fear, anger, and disgust, when exposed to certain food groups while non-breastfed infants displayed lower surprise and sadness reactions to their favorite foods and desserts. Further longitudinal research is needed to gain a comprehensive understanding of infants' emotional experiences and their associations with feeding behaviors and food acceptance.
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    Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests
    (2021) Cubukcu, Hikmet Can; Topcu, Deniz Ilhan; Bayraktar, Nilufer; Gulsen, Murat; Sari, Nuran; Arslan, Ayse Hande; 0000-0002-1219-6368; 0000-0002-7886-3688; 34791032; E-3717-2019; Y-8758-2018
    Objectives The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. Methods We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. Results The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). Conclusions ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.