| 1. |
- Berner, Jessica, et al.
(författare)
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Factors influencing Internet usage in older adults (65 years and above) living in rural and urban Sweden
- 2015
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Ingår i: Health Informatics Journal. - : Sage Publications. - 1460-4582 .- 1741-2811. ; 21:3, s. 237-249
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Tidskriftsartikel (refereegranskat)abstract
- Older adults living in rural and urban areas have shown to distinguish themselves in technology adoption; a clearer profile of their Internet use is important in order to provide better technological and health-care solutions. Older adults' Internet use was investigated across large to midsize cities and rural Sweden. The sample consisted of 7181 older adults ranging from 59 to 100 years old. Internet use was investigated with age, education, gender, household economy, cognition, living alone/or with someone and rural/urban living. Logistic regression was used. Those living in rural areas used the Internet less than their urban counterparts. Being younger and higher educated influenced Internet use; for older urban adults, these factors as well as living with someone and having good cognitive functioning were influential. Solutions are needed to avoid the exclusion of some older adults by a society that is today being shaped by the Internet.
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| 2. |
- Berner, Jessica, et al.
(författare)
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Maintaining cognitive function with internet use : a two-country, six-year longitudinal study
- 2019
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Ingår i: International psychogeriatrics. - : Cambridge University Press. - 1041-6102 .- 1741-203X. ; 31:7, s. 929-936
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Maintaining good cognitive function with aging may be aided by technology such as computers, tablets, and their applications. Little research so far has investigated whether internet use helps to maintain cognitive function over time.Design: Two population-based studies with a longitudinal design from 2001/2003 (T1) to 2007/2010 (T2).Setting: Sweden and the Netherlands.Participants: Older adults aged 66 years and above from the Swedish National Study on Ageing and Care (N = 2,564) and from the Longitudinal Aging Study Amsterdam (N = 683).Measurements: Internet use was self-reported. Using the scores from the Mini-Mental State Examination (MMSE) from T1 and T2, both a difference score and a significant change index was calculated. Linear and logistic regression analysis were performed with difference score and significant change index, respectively, as the dependent variable and internet use as the independent variable, and adjusted for sex, education, age, living situation, and functional limitations. Using a meta-analytic approach, summary coefficients were calculated across both studies.Results: Internet use at baseline was 26.4% in Sweden and 13.3% in the Netherlands. Significant cognitive decline over six years amounted to 9.2% in Sweden and 17.0% in the Netherlands. Considering the difference score, the summary linear regression coefficient for internet use was-0.32 (95% CI:-0.62,-0.02). Considering the significant change index, the summary odds ratio for internet use was 0.54 (95% CI: 0.37, 0.78).Conclusions: The results suggest that internet use might play a role in maintaining cognitive functioning. Further research into the specific activities that older adults are doing on the internet may shine light on this issue.
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| 3. |
- Davidsson, Paul, et al.
(författare)
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Distributed Monitoring and Control of Office Buildings by Embedded Agents
- 2005
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 171:4, s. 293-307
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Tidskriftsartikel (refereegranskat)abstract
- We describe a decentralized system consisting of a collection of software agents that monitor and control an office building. It uses the existing power lines for communication between the agents and the electrical devices of the building, such as sensors and actuators for lights and heating. The objectives are both energy saving and increasing customer satisfaction through value added services. Results of qualitative simulations and quantitative analysis based on thermodynamical modeling of an office building and its staff using four different approaches for controlling the building indicate that significant energy savings can result from using the agent-based approach. The evaluation also shows that customer satisfaction can be increased in most situations. The approach here presented makes it possible to control the trade-off between energy saving and customer satisfaction (and actually increase both, in comparison with current approaches).
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| 4. |
- Javeed, Ashir, 1989-, et al.
(författare)
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An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning
- 2022
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Ingår i: Life. - : MDPI. - 2075-1729. ; 12:7, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a neurological condition that primarily affects older adults and there is stillno cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 yearsbefore the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchershave presented several methods for early detection of dementia based on symptoms. However,these techniques suffer from two major flaws. The first issue is the bias of ML models caused byimbalanced classes in the dataset. Past research did not address this issue well and did not takepreventative precautions. Different ML models were developed to illustrate this bias. To alleviate theproblem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance thetraining process of the proposed ML model. The second issue is the poor classification accuracy ofML models, which leads to a limited clinical significance. To improve dementia prediction accuracy,we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boostmodel. The autoencoder is used to extract relevant features from the feature space and the Adaboostmodel is deployed for the classification of dementia by using an extracted subset of features. Thehyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimentalfindings reveal that the suggested learning system outperforms eleven similar systems which wereproposed in the literature. Furthermore, it was also observed that the proposed learning systemimproves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity.Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00%and specificity of 96.65%.
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| 5. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a (Formula presented.) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model ((Formula presented.) _RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model (Formula presented.) _RF achieved the highest accuracy of 94.59%. The proposed model (Formula presented.) _RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model (Formula presented.) _RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module ((Formula presented.)).
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| 6. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
- 2023
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Ingår i: Biomedicines. - : MDPI. - 2227-9059. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.
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| 7. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks
- 2023
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Ingår i: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 75:2, s. 2491-2508
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.
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| 8. |
- Ketzer, Daniel, 1988-, et al.
(författare)
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Driving and restraining forces for the implementation of the Agrophotovoltaics system technology : A system dynamics analysis
- 2020
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Ingår i: Journal of Environmental Management. - : Elsevier. - 0301-4797 .- 1095-8630. ; 270
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Tidskriftsartikel (refereegranskat)abstract
- The innovative Agrophotovoltaics (APV) system technology combines agricultural biomass and solar power production on the same site and aims at reducing the conflict between food and power production. Unrelated to this benefit, this technology may impact the landscape negatively and could thus be subject to public opposition and/or restraining frameworks. The presented study offers a System Dynamics (SD) approach, through Causal Loop Diagrams (CLDs) models, based on the results of citizen workshops, literature research, and expert discussions on the technology. A comprehensive analysis of the driving and restraining forces for the implementation of APV-technology and expected or potential impacts reveals influential factors. Hence, this SD approach identifies bottlenecks and conflicting objectives in the technology implementation that need to be further addressed. A key finding is that successful APV-projects would require stakeholder involvement to achieve greater local acceptance. When it comes to production on agricultural land, APV-systems may drive the land use efficiency to up to 186 percent when the PV-panels serve for protection against heat stress. On the other hand, altered precipitation patterns and impacts on agricultural cultivation and, especially, the landscape caused by the technical system, may restrain the application of APV. Finally, system design factors and operator modes are amongst the criteria that may influence the local acceptance in society, farmers’ motivation for APV and economic factors for the market launch of APV. © 2020 The Authors
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| 9. |
- Lagergren, Mårten, et al.
(författare)
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Horizontal and vertical target efficiency - a comparison between users and non-users of public long-term care in Sweden
- 2014
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Ingår i: Ageing & Society. - : Cambridge University Press. - 0144-686X .- 1469-1779. ; 34:4, s. 700-719
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Tidskriftsartikel (refereegranskat)abstract
- The extent to which a system of services is in tune with the needs of the population can be expressed in terms of target efficiency, which includes horizontal target efficiency - the extent to which those deemed to need a service receive it - and vertical target efficiency - the corresponding extent to which those who receive a service actually need it. Vertical efficiency can be measured by looking only at those receiving services. To measure horizontal target efficiency in a population, one must have access to population surveys. Data were taken from the baseline survey of the Swedish National Study on Ageing and Care (SNAC study). The results show that more than 80 per cent of those dependent in personal activities of daily living in the studied geographic areas were users of public long-term care (LTC). Dependency in instrumental activities of daily living was identified as the most important predictor of using LTC. Vertical target efficiency was 83-95 per cent depending on age, gender and type of household, if need was defined as dependency in instrumental activities of daily living. It was considerably lower, 35-61 per cent when defined as dependency in personal daily activities. Overall, long-term target efficiency in Sweden must be regarded as high. Few persons who need public LTC services fail to receive them.
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| 10. |
- Lagergren, Mårten, et al.
(författare)
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Horizontal and vertical targeting : a population-based comparison of public eldercare services in urban and rural areas of Sweden
- 2016
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Ingår i: Aging Clinical and Experimental Research. - : Springer Science and Business Media LLC. - 1594-0667 .- 1720-8319. ; 28:1, s. 147-158
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Tidskriftsartikel (refereegranskat)abstract
- The concepts of target efficiency can be used to assess the extent to which service provision is in line with the needs of the population. Horizontal target efficiency denotes the extent to which those deemed to need a service receive it and vertical target efficiency is the corresponding extent to which those who receive services actually need them. The aim of this study was to assess the target efficiency of the Swedish eldercare system and to establish whether target efficiencies differ in different geographical areas such as large urban, midsize urban and rural areas. Vertical efficiency was measured by studying those people who received eldercare services and was expressed as a percentage of those who received services who were functionally dependent. To measure horizontal target efficiency, data collected at baseline in the longitudinal population study SNAC (Swedish National study on Aging and Care) during the years 2001-2004 were used. The horizontal efficiency was calculated as the percentage of functionally dependent persons who received services. Functional dependency was measured as having difficulty with instrumental activities of daily living (IADL) and/or personal activities of daily living (PADL). Services included long-term municipal eldercare services (LTC). Horizontal target efficiency for the public LTC system was reasonably high in all three geographical areas, when using dependency in PADL as the measure of need (70-90 %), but efficiency was lower when the less restrictive measure of IADL dependency was used (40-50 %). In both cases, the target efficiency was markedly higher in the large urban and the rural areas than in the midsize urban areas. Vertical target efficiency showed the same pattern-it was almost 100 % in all areas for IADL dependency, but only 50-60 % for PADL dependency. Household composition differed in the areas studied as did the way public long-term care was provided to people living alone as compared to those co-habiting.
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