Technology and Health Care - Volume 31, issue 6

Authors: Abromavičius, Vytautas | Serackis, Artūras | Katkevičius, Andrius | Kazlauskas, Mantas | Sledevič, Tomyslav

Article Type: Research Article

Abstract: BACKGROUND: Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions. OBJECTIVE: The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance. METHODS: We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals. In addition, we analyzed different algorithmic selection features to design relevant features …for robust and accurate predictions. RESULTS: The study reveals promising results, with the highest scores achieved using 100 and 150 features. The corresponding values for accuracy, AUROC, and F1-Score are 0.9, 0.89, and 0.87, respectively, indicating the potential of physiological signals for accurate grade prediction. CONCLUSION: The findings of this study suggest practical applications in the field of education, where the use of physiological signals can help students cope with exam stress and improve their academic performance. The importance of feature selection and the use of appropriate models highlight the importance of engineering relevant features for precise and reliable predictions. Show more

Keywords: Grades prediction, physiological signals, feature selection, exam stress

DOI: 10.3233/THC-235015

Citation: Technology and Health Care, vol. 31, no. 6, pp. 2499-2511, 2023

Authors: Lycholip, Valentinas | Puronaitė, Roma | Skorniakov, Viktor | Navickas, Petras | Tarutytė, Gabrielė | Trinkūnas, Justas | Burneikaitė, Greta | Kazėnaitė, Edita | Jankauskienė, Augustina

Article Type: Research Article

Abstract: BACKGROUND: The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. The National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases. OBJECTIVE: To evaluate if NEWS can be an indicator …to measure COVID-19 patient status in-hospital. METHODS: We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models’ performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score. RESULTS: Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient’s age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score – 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage. CONCLUSION: Our model demonstrated incremental value for COVID-19 patient’s status evaluation. Show more

Keywords: COVID-19, early warning score, in-hospital mortality, health status, statistical models, machine learning, electronic health records

DOI: 10.3233/THC-235016

Citation: Technology and Health Care, vol. 31, no. 6, pp. 2513-2524, 2023