Faculty of Electrical Engineering and Information Technologies
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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
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Item type:Publication, Optimization of Grid-Connected Microgrids with Residential Prosumers Using an Improved Genetic Algorithm(2024); With the continuous increase of the microgrids’ implementation into the power system the problem of maintaining their stability and balance arises and the necessity to adopt an appropriate energy management system emerges. This paper analyses the optimization of the grid-connected microgrid, which consists of a photovoltaic generator, a wind generator, a battery, and residential prosumers. The paper presents the application of an improved genetic algorithm, which takes into consideration the voltage levels on the connection points of the generators and the prosumers, as well as the trading with the local grid. The proposed algorithm suggests the usage of the standard genetic algorithm with the improvement in the fitness and selection process. The results of the simulation are compared with the results obtained when using a standard genetic algorithm with five different types of selection. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modified genetic algorithm for unit commitment of grid connected microgrids under real time pricing conditions(International Journal on Information Technologies and Security, 2024-09-01); <jats:p>This paper introduces a modification of the genetic algorithm aimed at enhancing the selection process for reproducing the next generation. This modification accelerates the optimization process and improves the outcome. The case study analyzes a grid-connected microgrid comprising renewable energy sources, a battery storage system, prosumers with installed photovoltaic generators, and consumers. The effectiveness of the proposed modification is validated through comparison with two selection algorithms commonly used in the standard genetic algorithms.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mean‐Guided Elite Selection Genetic Algorithm for Multi‐Objective Optimization of Operational Costs and Voltage Control in Grid‐Connected Microgrids(Institution of Engineering and Technology (IET), 2025-12-29); <jats:title>ABSTRACT</jats:title> <jats:p>This paper presents a bi‐objective optimisation approach for grid‐connected microgrids, aiming to minimise operational costs and voltage deviation at the connection nodes of distributed energy resources and loads. Existing research typically addresses these objectives separately, and the simultaneous consideration of economic performance and voltage deviation in grid‐connected community microgrids with multiple generation resources remains in an early stage of development. To advance the research in this area, a novel mean‐guided elite selection genetic algorithm (MGES‐GA) is proposed to enhance the balance between convergence and diversity in multi‐objective optimisation. The proposed algorithm enhances the selection process by re‐evaluating low‐performing individuals through gene mixing with elite solutions, thereby preserving diversity and avoiding premature convergence. Comparative analysis of the MGES‐GA with the enhanced genetic algorithm, differential evolution with heuristic, and improved differential evolutionary optimisation algorithms demonstrates its superior performance in optimising the economic dispatch of a grid‐connected microgrid. In a bi‐objective comparison with state‐of‐the‐art algorithms, tested on a modified IEEE European low‐voltage test feeder and IEEE 33‐bus network, MGES‐GA demonstrates its effectiveness in balancing conflicting objectives by producing lower voltage deviations at comparable or lower costs.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cross-border DSM as a complement to storage and RES in congestion management markets(Elsevier BV, 2023-06) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Hybrid CNN-DSP Algorithm for Package Detection in Distance Maps(IEEE, 2023-10-12)