| 1. |
- Behrens, Anders, et al.
(författare)
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CoGNIT Automated Tablet Computer Cognitive Testing in Patients With Mild Cognitive Impairment : Feasibility Study
- 2022
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Ingår i: JMIR Formative Research. - : JMIR Publications Inc.. - 2561-326X. ; 6:3
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Tidskriftsartikel (refereegranskat)abstract
- Background: Early diagnosis of cognitive disorders is becoming increasingly important. Limited resources for specialist assessment and an increasing demographical challenge warrants the need for efficient methods of evaluation. In response, CoGNIT, a tablet app for automatic, standardized, and efficient assessment of cognitive function, was developed. Included tests span the cognitive domains regarded as important for assessment in a general memory clinic (memory, language, psychomotor speed, executive function, attention, visuospatial ability, manual dexterity, and symptoms of depression). Objective: The aim of this study was to assess the feasibility of automatic cognitive testing with CoGNIT in older patients with symptoms of mild cognitive impairment (MCI). Methods: Patients older than 55 years with symptoms of MCI (n=36) were recruited at the research clinic at the Blekinge Institute of Technology (BTH), Karlskrona, Sweden. A research nurse administered the Mini-Mental State Exam (MMSE) and the CoGNIT app on a tablet computer. Technical and testing issues were documented. Results: The test battery was completed by all 36 patients. One test, the four-finger-tapping test, was performed incorrectly by 42% of the patients. Issues regarding clarity of instructions were found in 2 tests (block design test and the one finger-tapping test). Minor software bugs were identified. Conclusions: The overall feasibility of automatic cognitive testing with the CoGNIT app in patients with symptoms of MCI was good. The study highlighted tests that did not function optimally. The four-finger-tapping test will be discarded, and minor improvements to the software will be added before further studies and deployment in the clinic. © 2022 JMIR Publications Inc.. All right reserved.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- Moraes, Ana Luiza Dallora, et al.
(författare)
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Multifactorial 10-year prior diagnosis prediction model of dementia
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:18, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis.
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| 5. |
- Behrens, Anders, et al.
(författare)
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Sleep disturbance predicts worse cognitive performance in subsequent years : A longitudinal population-based cohort study
- 2023
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Ingår i: Archives of gerontology and geriatrics (Print). - : Elsevier. - 0167-4943 .- 1872-6976. ; 106
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Tidskriftsartikel (refereegranskat)abstract
- Background: Poor sleep is a potential modifiable risk factor for later life development cognitive impairment. The aim of this study is to examine if subjective measures of sleep duration and sleep disturbance predict future cognitive decline in a population-based cohort of 60, 66, 72 and 78-year-olds with a maximal follow up time of 18 years. Methods: This study included participants from the Swedish National Study on Ageing and Care – Blekinge, with assessments 2001–2021. A cohort of 60 (n = 478), 66 (n = 623), 72 (n = 662) and 78 (n = 548) year-olds, were assessed at baseline and every 6 years until 78 years of age. Longitudinal associations between sleep disturbance (sleep scale), self-reported sleep duration and cognitive tests (Mini Mental State Examination and the Clock drawing test) were examined together with typical confounders (sex, education level, hypertension, hyperlipidemia, smoking status, physical inactivity and depression). Results: There was an association between sleep disturbance at age 60 and worse cognitive function at ages 60, 66 and 72 years in fully adjusted models. The association was attenuated after bootstrap-analysis for the 72-year-olds. The items of the sleep scale most predictive of later life cognition regarded nightly awakenings, pain and itching and daytime naps. Long sleep was predictive of future worse cognitive function. Conclusion: Sleep disturbance was associated with worse future cognitive performance for the 60-year-olds, which suggests poor sleep being a risk factor for later life cognitive decline. Questions regarding long sleep, waking during the night, pain and itching and daytime naps should be further explored in future research and may be targets for intervention.
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| 6. |
- Sandberg, Jacob, et al.
(författare)
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Relating Experienced To Recalled breathlessness Observational (RETRO) study : A prospective study using a mobile phone application
- 2019
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Ingår i: BMJ Open Respiratory Research. - : BMJ Publishing Group. - 2052-4439. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Breathlessness, the subjective sensation of breathing discomfort, is common and appears in the daily life of people with cardiorespiratory diseases. Physicians often rely on patient's history based on symptom recall. The relation between recalled and experienced breathlessness is still poorly understood. This paper presents the protocol for a study primarily aimed at evaluating the relationship between experienced breathlessness and (1) recalled breathlessness and (2) predicted future breathlessness. Methods: A mobile phone application will be used to collect data during daily life. Medically stable participants, ≥18 years of age with mean daily breathlessness of Numerical Rating Scale (NRS) 3/10 and able to use a mobile phone with internet will rate their breathlessness intensity on a 0-10 NRS prompted the user several times daily for 1 week. Participants will recall their breathlessness each day and week. Multivariable random effects regression models will be used for statistical analyses. Results: Results of the study will be submitted for publication in peer-reviewed journals and presented at relevant conferences. Discussion: This protocol describes a study aimed at investigating previously unknown areas of the experience and recall of breathlessness using a new method of data collection. © 2019 Author(s).
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| 7. |
- Jusufi, Ilir, et al.
(författare)
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Visualization of spiral drawing data of patients with Parkinson's disease
- 2014
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Ingår i: IEEE International Conference on Information Visualization. - : IEEE Press. - 9781479941032 ; , s. 346-350, s. 346-350, s. 346-350
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Konferensbidrag (refereegranskat)abstract
- Patients with Parkinson's disease (PD) need to be frequently monitored in order to assess their individual symptoms and treatment-related complications. Advances in technology have introduced telemedicine for patients in remote locations. However, data produced in such settings lack much information and are not easy to analyze or interpret compared to traditional, direct contact between the patient and clinician. Therefore, there is a need to present the data using visualization techniques in order to communicate in an understandable and objective manner to the clinician. This paper presents interaction and visualization approaches used to aid clinicians in the analysis of repeated measures of spirography of PD patients gathered by means of a telemetry touch screen device. The proposed approach enables clinicians to observe fine motor impairments and identify motor fluctuations of their patients while they perform the tests from their homes using the telemetry device.
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| 8. |
- Juuso, Päivi, et al.
(författare)
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The Workplace Experiences of Women with Fibromyalgia
- 2016
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Ingår i: Musculoskeletal Care. - : Wiley. - 1478-2189 .- 1557-0681. ; 14:2, s. 69-76
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Fibromyalgia (FM) is a common pain syndrome that mostly affects women. Chronic pain and other symptoms often challenge work for women with FM. This study aimed to explore how women with FM experience their work situations.Method: A purposive sample of 15 women with FM was interviewed with in-depth qualitative interviews. Data were analysed using a hermeneutic approach.Results: The results revealed that women with FM experienced incapacity to work as they had previously and eventually accepted that their work life had changed or reached its end. Since their work had great significance in their lives, feelings of loss and sorrow were common. Women who were working, unemployed, or on sick leave described feelings of fear for their future work situations.Conclusions: Women with FM greatly value their work. However, their wishes to perform at work as they had previously and their ability to do so fail to conform. As such, women with FM need support in continuing to work for as long as possible, after which they need support in finding new values in life.
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| 9. |
- Memedi, Mevludin, 1983-, et al.
(författare)
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Visualization of spirography-based objective measures in Parkinson's disease
- 2014
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Ingår i: Movement Disorders Supplement. - : Wiley-Blackwell. - 0885-3185. ; , s. S187-S189
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objective: To investigate whether advanced visualizations of spirography-based objective measures are useful in differentiating motor complications among Parkinson’s disease (PD) patients.Background: Sixty-five patients diagnosed with advanced PD have utilized a telemetry test battery, implemented on a touch screen handheld computer, in a telemedicine setting. On each test occasion, they were asked to perform repeated and time-stamped assessments of spiral drawing performance by tracing a pre-drawn Archimedes spiral. The test battery was also used by 10 healthy elderly (HE) subjects.Methods: A web-based framework was developed to visualize the performance during spirography of both patients and HE subjects to a clinician (DN). The performance was depicted by animating the spiral drawings (Fig 1). In addition, the framework displayed two time series views for representing drawing speed (blue line) and displacement from the ideal trajectory (orange line). The views are coordinated and linked i.e. user interactions in one of the views will be reflected in other views. For instance, when the user points in one of the pixels in spiral view, the circle size of the underlying pixel increases and a vertical line appears in the time series views to depict the corresponding position. Fig 1 shows single randomly selected spirals per each subject group: A) a PD patient in Dyskinesia state, B) a HE subject, and C) a PD patient in Off state.Results: The clinician recognized Dyskinesia symptoms as movements made with high speed, smooth/gradual spatial displacements, and a small amount of hesitation (Fig 1A). Similarly, Off symptoms were associated with low speed, sharp/abrupt spatial displacements, and a large amount of hesitation (Fig 1C). In contrast, the spiral drawn by a HE subject (Fig 1B) was associated with unchanging levels of kinematic features i.e. drawing speed, spatial displacements and hesitation over time.Conclusions: Visualizing spirography-based objective measures enables identification of trends and patterns of motor dysfunctions at the patient’s individual level. Dynamic access of visualized motor tests may be useful during the evaluation of therapy-related complications such as under- and over-medications. This will assist during individualized optimization of therapies, enabling patients to spend more time in the On state with a minimum of Off and dyskinetic states.
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