| 1. |
- Agvall, B., et al.
(author)
-
Characteristics, management and outcomes in patients with CKD in a healthcare region in Sweden: a population-based, observational study
- 2023
-
In: Bmj Open. - London : BMJ Publishing Group Ltd. - 2044-6055. ; 13:7
-
Journal article (peer-reviewed)abstract
- ObjectivesTo describe chronic kidney disease (CKD) regarding treatment rates, comorbidities, usage of CKD International Classification of Diseases (ICD) diagnosis, mortality, hospitalisation, evaluate healthcare utilisation and screening for CKD in relation to new nationwide CKD guidelines. DesignPopulation-based observational study. SettingHealthcare registry data of patients in Southwest Sweden. ParticipantsA total cohort of 65 959 individuals aged >18 years of which 20 488 met the criteria for CKD (cohort 1) and 45 470 at risk of CKD (cohort 2). Primary and secondary outcome measuresData were analysed with regards to prevalence, screening rates of blood pressure, glucose, estimated glomerular filtration rate (eGFR), Urinary-albumin-creatinine ratio (UACR) and usage of ICD-codes for CKD. Mortality and hospitalisation were analysed with logistic regression models. ResultsOf the CKD cohort, 18% had CKD ICD-diagnosis and were followed annually for blood pressure (79%), glucose testing (76%), eGFR (65%), UACR (24%). UACR follow-up was two times as common in hypertensive and cardiovascular versus diabetes patients with CKD with a similar pattern in those at risk of CKD. Statin and renin-angiotensin-aldosterone inhibitor appeared in 34% and 43%, respectively. Mortality OR at CKD stage 5 was 1.23 (CI 0.68 to 0.87), diabetes 1.20 (CI 1.04 to 1.38), hypertension 1.63 (CI 1.42 to 1.88), atherosclerotic cardiovascular disease (ASCVD) 1.84 (CI 1.62 to 2.09) associated with highest mortality risk. Hospitalisation OR in CKD stage 5 was 1.96 (CI 1.40 to 2.76), diabetes 1.15 (CI 1.06 to 1.25), hypertension 1.23 (CI 1.13 to 1.33) and ASCVD 1.52 (CI 1.41 to 1.64). ConclusionsThe gap between patients with CKD by definition versus those diagnosed as such was large. Compared with recommendations patients with CKD have suboptimal follow-up and treatment with renin-angiotensin-aldosterone system inhibitor and statins. Hypertension, diabetes and ASCVD were associated with increased mortality and hospitalisation. Improved screening and diagnosis of CKD, identification and management of risk factors and kidney protective treatment could affect clinical and economic outcomes.
|
|
| 2. |
- Alavijeh, Soroush Zamani, et al.
(author)
-
What users’ musical preference on Twitter reveals about psychological disorders
- 2023
-
In: Information Processing & Management. - London : Elsevier. - 0306-4573 .- 1873-5371. ; 60:3
-
Journal article (peer-reviewed)abstract
- Previous research found a strong relation between the users’ psychological disorders and their language use in social media posts in terms of vocabulary selection, emotional expressions, and psychometric attributes. However, although studying the association between psychological disorders and musical preference is considered as rather an old tradition in the clinical analysis of health data, it is not explored through the lens of social media analytics. In this study, we investigate which attributes of the music posted on social media are associated with mental health conditions of Twitter users. We created a large-scale dataset of 1519 Twitter users with six self-reported psychological disorders (depression, bipolar, anxiety, panic, post-traumatic stress disorder, and borderline) and matched with 2480 control users. We then conduct an observational study to investigate the relationship between the users’ psychological disorders and their musical preference by analyzing lyrics of the music tracks that the users shared on Twitter from multiple dimensions including word usage, linguistic style, sentiment and emotion patterns, topical interests and underlying semantics. Our findings reveal descriptive differences on the linguistic and semantic features of music tracks of affected users compared to control individuals and among users from different psychological disorders. Additionally, we build a feature-based and an (explainable) deep learning-based binary classifiers trained on disorder and control users and demonstrate that lyrics of the music tracks of users on Twitter can be considered as complementary information to their published posts to improve the accuracy of the disorder detection task. Overall, we find that the music attributes of users on Twitter allow inferences about their mental health status. © 2023 Elsevier Ltd
|
|
| 3. |
- Amirahmadi, Ali, 1994-, et al.
(author)
-
A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning
- 2023
-
In: Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. - Amsterdam : IOS Press. - 0926-9630 .- 1879-8365. - 9781643683881 ; 302, s. 609-610, s. 609-610
-
Conference paper (peer-reviewed)abstract
- Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).
|
|
| 4. |
- Amirahmadi, Ali, 1994-, et al.
(author)
-
Deep learning prediction models based on EHR trajectories : A systematic review
- 2023
-
In: Journal of Biomedical Informatics. - Maryland Heights, MO : Academic Press. - 1532-0464 .- 1532-0480. ; 144
-
Research review (peer-reviewed)abstract
- Background: : Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients’ future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions. Methods: For this systematic review, we searched Scopus, PubMed, IEEE Xplore, and ACM databases from Jan 2016 to April 2022 using search terms centered around EHR, deep learning, and trajectories. Then the selected papers were analyzed according to publication characteristics, objectives, and their solutions regarding existing challenges, such as the model's capacity to deal with intricate data dependencies, data insufficiency, and explainability. Results: : After removing duplicates and out-of-scope papers, 63 papers were selected, which showed rapid growth in the number of research in recent years. Predicting all diseases in the next visit and the onset of cardiovascular diseases were the most common targets. Different contextual and non-contextual representation learning methods are employed to retrieve important information from the sequence of EHR trajectories. Recurrent neural networks and the time-aware attention mechanism for modeling long-term dependencies, self-attentions, convolutional neural networks, graphs for representing inner visit relations, and attention scores for explainability were frequently used among the reviewed publications. Conclusions: This systematic review demonstrated how recent breakthroughs in deep learning methods have facilitated the modeling of EHR trajectories. Research on improving the ability of graph neural networks, attention mechanisms, and cross-modal learning to analyze intricate dependencies among EHRs has shown good progress. There is a need to increase the number of publicly available EHR trajectory datasets to allow for easier comparison among different models. Also, very few developed models can handle all aspects of EHR trajectory data. © 2023 The Author(s)
|
|
| 5. |
- Budu, Emmanuella, 1995-, et al.
(author)
-
A Framework for Evaluating Synthetic Electronic Health Records
- 2023
-
In: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683881 - 9781643683898 ; , s. 378-379
-
Conference paper (peer-reviewed)abstract
- Synthetic data generation can be applied to Electronic Health Records (EHRs) to obtain synthetic versions that do not compromise patients' privacy. However, the proliferation of synthetic data generation techniques has led to the introduction of a wide variety of methods for evaluating the quality of generated data. This makes the task of evaluating generated data from different models challenging as there is no consensus on the methods used. Hence the need for standard ways of evaluating the generated data. In addition, the available methods do not assess whether dependencies between different variables are maintained in the synthetic data. Furthermore, synthetic time series EHRs (patient encounters) are not well investigated, as the available methods do not consider the temporality of patient encounters. In this work, we present an overview of evaluation methods and propose an evaluation framework to guide the evaluation of synthetic EHRs. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
|
|
| 6. |
- Davidge, Jason, et al.
(author)
-
Clinical characteristics at hospital discharge that predict cardiovascular readmission within 100 days in heart failure patients – An observational study
- 2023
-
In: International Journal of Cardiology Cardiovascular Risk and Prevention. - Philadelphia, PA : Elsevier. - 2772-4875. ; 16
-
Journal article (peer-reviewed)abstract
- Background: After a heart failure (HF) hospital discharge, the risk of a cardiovascular (CV) related event is highest in the following 100 days. It is important to identify factors associated with increased risk of readmission. Method: This retrospective, population-based study examined HF patients in Region Halland (RH), Sweden, hospitalized with a HF diagnosis between 2017 and 2019. Data regarding patient clinical characteristics were retrieved from the Regional healthcare Information Platform from admission until 100 days post-discharge. Primary outcome was readmission due to a CV related event within 100 days. Results: There were 5029 included patients being admitted for HF and discharged and 1966 (39%) were newly diagnosed. Echocardiography was available for 3034 (60%) patients and 1644 (33%) had their first echocardiography while admitted. The distribution of HF-phenotypes was 33% HF with reduced ejection fraction (EF), 29% HF with mildly reduced EF and 38% HF with preserved EF. Within 100 days, 1586 (33%) patients were readmitted, and 614 (12%) died. A Cox regression model showed that advanced age, longer hospital length of stay, renal impairment, high heart rate and elevated NT-proBNP were associated with an increased risk of readmission regardless of HF-phenotype. Women and increased blood pressure are associated with a reduced risk of readmission. Conclusions: One third had a CV-readmission within 100 days. This study found clinical factors already present at discharge that are associated with increased risk of readmission which should be considered at discharge. © 2023 The Authors
|
|
| 7. |
- Etminani, Kobra, 1984-, et al.
(author)
-
A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
- 2022
-
In: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
-
Journal article (peer-reviewed)abstract
- Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
|
|
| 8. |
- Etminani, Kobra, 1984-, et al.
(author)
-
How Behavior Change Strategies are Used to Design Digital Interventions to Improve Medication Adherence and Blood Pressure Among Patients With Hypertension : Systematic Review
- 2020
-
In: Journal of Medical Internet Research. - Toronto : J M I R Publications, Inc.. - 1438-8871. ; 22:4
-
Journal article (peer-reviewed)abstract
- Background: Information on how behavior change strategies have been used to design digital interventions (DIs) to improve blood pressure (BP) control or medication adherence (MA) for patients with hypertension is currently limited.Objective: Hypertension is a major modifiable risk factor for cardiovascular diseases and can be controlled with appropriate medication. Many interventions that target MA to improve BP are increasingly using modern digital technologies. This systematic review was conducted to discover how DIs have been designed to improve MA and BP control among patients with hypertension in the recent 10 years. Results were mapped into a matrix of change objectives using the Intervention Mapping framework to guide future development of technologies to improve MA and BP control.Methods: We included all the studies regarding DI development to improve MA or BP control for patients with hypertension published in PubMed from 2008 to 2018. All the DI components were mapped into a matrix of change objectives using the Intervention Mapping technique by eliciting the key determinant factors (from patient and health care team and system levels) and targeted patient behaviors.Results: The analysis included 54 eligible studies. The determinants were considered at two levels: patient and health care team and system. The most commonly described determinants at the patient level were lack of education, lack of self-awareness, lack of self-efficacy, and forgetfulness. Clinical inertia and an inadequate health workforce were the most commonly targeted determinants at the health care team and system level. Taking medication, interactive patient-provider communication, self-measurement, and lifestyle management were the most cited patient behaviors at both levels. Most of the DIs did not include support from peers or family members, despite its reported effectiveness and the rate of social media penetration.Conclusions: This review highlights the need to design a multifaceted DI that can be personalized according to patient behavior(s) that need to be changed to overcome the key determinant(s) of low adherence to medication or uncontrolled BP among patients with hypertension, considering different levels including patient and healthcare team and system involvement. © Kobra Etminani, Arianna Tao Engström, Carina Göransson, Anita Sant’Anna, Sławomir Nowaczyk.
|
|
| 9. |
- Etminani, Kobra, 1984-, et al.
(author)
-
Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension : Protocol for an Interrupted Time Series Study
- 2021
-
In: JMIR Research Protocols. - Toronto : JMIR. - 1929-0748. ; 10:5
-
Journal article (peer-reviewed)abstract
- Background: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension.Objective: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension.Methods: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study.Results: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it.Conclusions: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff. © Kobra Etminani, Carina Göransson, Alexander Galozy, Margaretha Norell Pejner, Sławomir Nowaczyk.
|
|
| 10. |
- Etminani, Kobra, 1984-, et al.
(author)
-
Peeking inside the box : Transfer Learning vs 3D convolutional neural networks applied in neurodegenerative diseases
- 2021
-
In: Proceedings of CIBB 2021.
-
Conference paper (peer-reviewed)abstract
- Convolutional Neural Networks (CNNs) have shown their effectiveness in a variety of imaging applications including medical imaging diagnostics. However, these deep learning models are data-hungry and need enough labeled samples for the training phase which is limited in the medical domain. Transfer learning is one possible solution to this challenge with training a new model. Assessing model performance should be done not only based on criteria like accuracy, and area under the ROC curve, but also it is important to investigate what regions were of most interest for the classification decisions, especially for medical applications. We performed a case study on neurodegenerative disorders, in specific Alzheimer’s disease, mild cognitive im- pairment, dementia with lewy bodies and cognitively normal brains using 3D 18F-FDG-PET brain scans. Two transfer learning models, InceptionV3 and ResNet50, as well as a custom 3D-CNN that is trained from scratch are compared. Two XAI methods, occlusion and Grad-CAM are chosen to visualize the important brain regions using correctly classified cases. We found that the TL models learn significantly different decision surfaces than the 3D-CNN model. The 3D spatial structure of the brain regions are better kept in the 3D-CNN model, and that might explain the higher performance of this model over 2D-TL models. Moreover, we found out the two XAI methods provide different results, where occlusion method focused more on specific brain regions.
|
|
