Synthetic data generation has been proposed as a potential solution to accessing Electronic Health Records (EHRs) while minimizing the privacy risks associated with real EHRs. Nevertheless, the practical use of synthetic EHRs rests on their ability to resemble the quality of real EHRs. Existing evaluations of synthetic EHRs often focus on assessing them as static snapshots frozen in time, neglecting temporal dependencies and varying temporal patterns. Moreover, some of these methods rely on subjective judgments, are limited to segmentable time-series, and employ methods that adopt a one-to-one approach. This study employs a comprehensive approach to evaluating fidelity in synthetic time-series EHRs to address these challenges. We extend the functionality of time-series analysis methods such as temporal clustering, time-series similarity measures, Sample Entropy, and trend analysis, to evaluate varying temporal patterns in synthetic time-series EHRs. Our findings provide valuable insights into how synthetic EHRs align with real EHRs in the temporal context, considering aspects such as patient groupings, temporal dynamics, predictability, and directional change. We empirically demonstrate the feasibility of assessing temporal fidelity with these methods, offering an understanding of the quality of synthetic EHRs in capturing the varying temporal patterns inherent in EHRs. © 2024 IEEE.

Recent studies have shown how synthetic data generation methods can be applied to electronic health records (EHRs) to obtain synthetic versions that do not violate privacy rules. This growing body of research has resulted in the emergence of numerous methods for evaluating the quality of generated data, with new publications often introducing novel evaluation methods. This work presents a detailed review of synthetic EHRs, focusing on the various evaluation methods used to assess the quality of the generated EHRs. We discuss the existing evaluation methods, offering insights into their use as well as providing an interpretation of the evaluation metrics from the perspectives of achieving fidelity, utility and privacy. Furthermore, we highlight the key factors influencing the selection of evaluation methods, such as the type of data (e.g., categorical, continuous, or discrete) and the mode of application (e.g., patient level, cohort level, and feature level). To assess the effectiveness of current evaluation measures, we conduct a series of experiments to shed light on the potential limitations of these measures. The findings from these experiments reveal notable shortcomings, including the need for meticulous application of methods to the data to reduce inconsistent evaluations, the qualitative nature of some assessments subject to individual judgment, the need for clinical validations, and the absence of techniques to evaluate temporal dependencies within the data. This highlights the need to place greater emphasis on evaluation measures, their application, and the development of comprehensive evaluation frameworks as it is crucial for advancing progress in this field. © 2024 The Author(s)

Objective: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. Methods: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. Results: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. Conclusion: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care. © 2024 The Authors

In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships. The construction of large KGs can enable the integration of heterogeneous information sources and help Artificial Intelligence (AI) systems be more explainable and interpretable. This systematic review examines a selection of recent publications to understand how KGs are currently being used in eXplainable AI systems. To achieve this goal, we design a framework and divide the use of KGs into four categories: extracting features, extracting relationships, constructing KGs, and KG reasoning. We also identify where KGs are mostly used in eXplainable AI systems (pre-model, in-model, and post-model) according to the aforementioned categories. Based on our analysis, KGs have been mainly used in pre-model XAI for feature and relation extraction. They were also utilised for inference and reasoning in post-model XAI. We found several studies that leveraged KGs to explain the XAI models in the healthcare domain. © The Author(s) 2022.

INTRODUCTION: Atrial fibrillation (AF) is the most common arrhythmia and confers an increased risk of mortality, stroke, heart failure and cognitive decline. There is growing interest in AF screening; however, the most suitable population and device for AF detection remains to be elucidated. Here, we present the design of the CONSIDERING-AF (deteCtiON and Stroke preventIon by moDEl scRreenING for Atrial Fibrillation) study. METHODS AND ANALYSIS: CONSIDERING-AF is a randomised, controlled, siteless, non-blinded diagnostic superiority trial with four parallel groups and a primary endpoint of identifying AF during a 6-month study period set in Region Halland, Sweden. In each group, 740 individuals aged≥65 years will be included. The primary objective is to compare the intervention of AF screening enrichment using a risk prediction model (RPM), followed by 14 days of a continuous ECG patch, with no intervention (standard care). Primary outcome is defined as the incident AF recorded in the Region Halland Information Database after 6 months as compared with standard care. Secondary endpoints include the difference in incident AF between groups enriched or not by the RPM, with and without an invitation to 14 days of continuous ECG recording, and the proportions of oral anticoagulation treatment in the four groups. ETHICS AND DISSEMINATION: This study has ethical approval from the Swedish Ethical Review Authority. Results will be published in peer-reviewed international journals. TRIAL REGISTRATION NUMBER: NCT05838781. © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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.

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). © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

Objectives: To 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.

Design: Population-based observational study.

Setting: Healthcare registry data of patients in Southwest Sweden.

Participants: A 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 measures: Data 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.

Results: Of 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).

Conclusions: The 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. © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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

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)