| 1. |
- Agvall, B., et al.
(author)
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Characteristics, management and outcomes in patients with CKD in a healthcare region in Sweden: a population-based, observational study
- 2023
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In: Bmj Open. - London : BMJ Publishing Group Ltd. - 2044-6055. ; 13:7
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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.
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| 2. |
- Alavijeh, Soroush Zamani, et al.
(author)
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What users’ musical preference on Twitter reveals about psychological disorders
- 2023
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In: Information Processing & Management. - London : Elsevier. - 0306-4573 .- 1873-5371. ; 60:3
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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
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| 3. |
- Amirahmadi, Ali, 1994-, et al.
(author)
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A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning
- 2023
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In: Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. - Amsterdam : IOS Press. - 1879-8365 .- 0926-9630. - 9781643683881 ; 302, s. 609-610, s. 609-610
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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).
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| 4. |
- Amirahmadi, Ali, 1994-, et al.
(author)
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Deep learning prediction models based on EHR trajectories : A systematic review
- 2023
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In: Journal of Biomedical Informatics. - Maryland Heights, MO : Academic Press. - 1532-0464 .- 1532-0480. ; 144
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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)
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| 5. |
- Budu, Emmanuella, 1995-, et al.
(author)
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A Framework for Evaluating Synthetic Electronic Health Records
- 2023
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In: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683881 - 9781643683898 ; , s. 378-379
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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.
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| 6. |
- Davidge, Jason, et al.
(author)
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Clinical characteristics at hospital discharge that predict cardiovascular readmission within 100 days in heart failure patients – An observational study
- 2023
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In: International Journal of Cardiology Cardiovascular Risk and Prevention. - Philadelphia, PA : Elsevier. - 2772-4875. ; 16
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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
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| 7. |
- Hamed, Omar, 1979-, et al.
(author)
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Temporal Context Matters : An Explainable Model for Medical Resource Utilization in Chronic Kidney Disease
- 2023
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In: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683881 - 9781643683898 ; , s. 613-614
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Conference paper (peer-reviewed)abstract
- The prediction of medical resource utilization is beneficial for effective healthcare resource planning and allocation. Previous work in resource utilization prediction can be categorized into two main classes, count-based and trajectory-based. Both of these classes have some challenges, in this work we propose a hybrid approach to overcome these challenges. Our initial results promote the value of temporal context in resource utilization prediction and highlight the importance of model explainability in understanding the main important variables. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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| 8. |
- Hashemi, Atiye Sadat, 1991-, et al.
(author)
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Domain Knowledge-Driven Generation of Synthetic Healthcare Data
- 2023
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In: Caring is Sharing – Exploiting the Value in Data for Health and Innovation. - Amsterdam : IOS Press. - 9781643683898 ; , s. 352-353
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Conference paper (peer-reviewed)abstract
- Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility, fidelity, and clinical validity while preserving patient privacy. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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| 9. |
- Hashemi, Atiye Sadat, 1991-, et al.
(author)
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Time-series Anonymization of Tabular Health Data using Generative Adversarial Network
- 2023
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In: 2023 International Joint Conference on Neural Networks (IJCNN). - Piscataway, NJ : IEEE. - 9781665488679 - 9781665488686
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Conference paper (peer-reviewed)abstract
- Data anonymization has been used as a fundamental tool in various domains, e.g. healthcare, to alter personal data such that individuals can no longer be identified directly or indirectly in a way to enable broader sharing of data. For example, data perturbation techniques add noise to original data allowing individual record confidentiality while maintaining high-quality data for analytical purposes. In this paper, we propose a perturbation technique for anonymizing longitudinal tabular data such as electronic health records (EHRs). Our model starts by learning a latent space of original data to better capture temporal trends, then employs a generative adversarial network together to train a perturbation generator. During model training, a time-supervised loss function for handling sequence-dependent noise, together with the adversarial unsupervised, anonymization, and reconstruction loss functions are utilized. To evaluate our model quantitatively, we use multiple evaluation metrics for the fidelity, utility, and identifiability of generated data, in addition, the model is evaluated qualitatively by visualizing generated and original data. The results confirm that our model preserves the privacy of the original data and generates a perturbed version with high fidelity and utility compared to some state-of-the-art techniques. © 2023 IEEE.
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| 10. |
- Soliman, Amira, 1980-, et al.
(author)
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Interdisciplinary Human-Centered AI for Hospital Readmission Prediction of Heart Failure Patients
- 2023
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In: Caring is sharing - exploiting the value in data for health and innovation. - Amsterdam : IOS Press. - 9781643683881 ; , s. 556-560
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Conference paper (peer-reviewed)abstract
- The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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