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1. EHR Security and Privacy Aspects: A Systematic Review

   Banerjee, Sourav; Barik, Sudip; Das, Debashis; Ghosh, Uttam (October 2023, Springer Nature Switzerland)

   Electronic Health Records (EHRs) have become increasingly popular in recent years, providing a convenient way to store, manage and share relevant information among healthcare providers. However, as EHRs contain sensitive personal information, ensuring their security and privacy is most important. This paper reviews the key aspects of EHR security and privacy, including authentication, access control, data encryption, auditing, and risk management. Additionally, the paper dis- cusses the legal and ethical issues surrounding EHRs, such as patient consent, data ownership, and breaches of confidentiality. Effective implementation of security and privacy measures in EHR systems requires a multi-disciplinary approach involving healthcare providers, IT specialists, and regulatory bodies. Ultimately, the goal is to come upon a balance between protecting patient privacy and ensuring timely access to critical medical information for feature healthcare delivery. 

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2. Generating Connected Synthetic Electronic Health Records and Social Media Data for Modeling and Simulation

   Tall, Anne M.; Zou, Cliff C.; Wang, Jun (December 2020, Interservice/Industry Training, Simulation and Education Conference (I/ITSEC))

   null (Ed.)

   Research and experimentation using big data sets, specifically large sets of electronic health records (EHR) and social media data, is demonstrating the potential to understand the spread of diseases and a variety of other issues. Applications of advanced algorithms, machine learning, and artificial intelligence indicate a potential for rapidly advancing improvements in public health. For example, several reports indicate that social media data can be used to predict disease outbreak and spread (Brown, 2015). Since real-world EHR data has complicated security and privacy issues preventing it from being widely used by researchers, there is a real need to synthetically generate EHR data that is realistic and representative. Current EHR generators, such as Syntheaä (Walonoski et al., 2018) only simulate and generate pure medical-related data. However, adding patients’ social media data with their simulated EHR data would make combined data more comprehensive and realistic for healthcare research. This paper presents a patients’ social media data generator that extends an EHR data generator. By adding coherent social media data to EHR data, a variety of issues can be examined for emerging interests, such as where a contagious patient may have been and others with whom they may have been in contact. Social media data, specifically Twitter data, is generated with phrases indicating the onset of symptoms corresponding to the synthetically generated EHR reports of simulated patients. This enables creation of an open data set that is scalable up to a big-data size, and is not subject to the security, privacy concerns, and restrictions of real healthcare data sets. This capability is important to the modeling and simulation community, such as scientists and epidemiologists who are developing algorithms to analyze the spread of diseases. It enables testing a variety of analytics without revealing real-world private patient information. 

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3. Recent Advances in Predictive Modeling with Electronic Health Records

   https://doi.org/10.24963/ijcai.2024/914  

   Wang, Jiaqi; Luo, Junyu; Ye, Muchao; Wang, Xiaochen; Zhong, Yuan; Chang, Aofei; Huang, Guanjie; Yin, Ziyi; Xiao, Cao; Sun, Jimeng; et al (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machine learning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research. 

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4. A new representation of disease conditions and treatment pathways accurately predicts mortality and chronic diseases

   Ngufor, Che; Caraballo, Pedro; Byrne, Thomas J.; Chen, David; Shah, Nilay D.; Steinbach, Michael; Simon, Gyorgy (November 2019, AMIA 2019 Annual Symposium)

   In this study, we introduce a novel representation of patient data called Disease Severity Hierarchy (DSH) that explores specific diseases and their known treatment pathways in a nested fashion to create subpopulations in a clinically meaningful way. As the DSH tree is traversed from the root towards the leaves, we encounter subpopulations that share increasing richer amounts of clinical details such as similar disease severity, illness trajectories, and time to event that are discriminative, and suitable for learning risk stratification models. The proposed DSH risk scores effectively and accurately predict the age at which a patient may be at risk of dying or developing MCE significantly better than a traditional representation of disease conditions. DSH utilizes known relationships among various entities in EHR data to capture disease severity in a natural way and has the additional benefit of being expressive and interpretable. This novel patient representation can help support critical decision making, development of smart EBP guidelines, and enhance healthcare care and disease management by helping to identify and reduce disease burden among high-risk patients. 

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5. Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

   Lu, Chang; Han, Tian; Ning, Yue (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient's visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events. 

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