Faculty of Electrical Engineering and Information Technologies
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Item type:Publication, Deep Learning for Facial Expression and Human Activity Recognition Using Smart Glasses(Institute of Electrical and Electronics Engineers (IEEE), 2025-03-14) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bias in vital signs? Machine learning models can learn patients’ race or ethnicity from the values of vital signs alone(BMJ, 2025-07-10); ; ; ; Mullan, Irene DankwaObjectives To investigate whether machine learning (ML) algorithms can learn racial or ethnic information from the vital signs alone. Methods A retrospective cohort study of critically ill patients between 2014 and 2015 from the multicentre eICU-CRD critical care database involving 335 intensive care units in 208 US hospitals, containing 200 859 admissions. We extracted 10 763 critical care admissions of patients aged 18 and over, alive during the first 24 hours after admission, with recorded race or ethnicity as well as at least two measurements of heart rate, oxygen saturation, respiratory rate and blood pressure. Pairs of subgroups were matched based on age, gender, admission diagnosis and disease severity. XGBoost, Random Forest and Logistic Regression algorithms were used to predict recorded race or ethnicity based on the values of vital signs. Results Models derived from only four vital signs can predict patients’ recorded race or ethnicity with an area under the curve (AUC) of 0.74 (±0.030) between White and Black patients, AUC of 0.74 (±0.030) between Hispanic and Black patients and AUC of 0.67 (±0.072) between Hispanic and White patients, even when controlling for known factors. There were very small, but statistically significant differences between heart rate, oxygen saturation and blood pressure, but not respiration rate and invasively measured oxygen saturation. Discussion ML algorithms can extract racial or ethnicity information from vital signs alone across diverse patient populations, even when controlling for known biases such as pulse oximetry variations and comorbidities. The model correctly classified the race or ethnicity in two out of three patients, indicating that this outcome is not random. Conclusion Vital signs embed racial information that can be learnt by ML algorithms, posing a significant risk to equitable clinical decision-making. Mitigating measures might be challenging, considering the fundamental role of vital signs in clinical decision-making. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, FedMMA-HAR: Federated Learning for Human Activity Recognition With Missing Modalities Using Head-Worn Wearables(Institute of Electrical and Electronics Engineers (IEEE), 2024-10) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data Science and Machine Learning Teaching Practices with Focus on Vocational Education and Training(Vilnius University Press, 2023-04-19); ; Dimitrovska, Marija Markovska<jats:p>With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Federated Learning for Activity Recognition: A System Level Perspective(IEEE, 2023-04); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 11th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)(ACM, 2023-10-08) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Facial expression recognition using facial mask with EMG sensors(2023-05) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Therapeutics Evolution What kind of Research Will Make the Difference in this Area?(ACM, 2023-10-08) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Summary of SHL challenge 2023: Recognizing locomotion and transportation mode from GPS and motion sensors(2023-09) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smart Glasses for Gait Analysis of Parkinson’s Disease Patients(IEEE, 2023-05-22)