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Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations.
Annu Rev Biomed Data Sci
January 2025
2Departments of Bioengineering and Genetics, Stanford University, Stanford, California, USA.
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data.
View Article and Find Full Text PDFMoving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.
Health Data Sci
December 2024
Department of Clinical Epidemiology and Biostatistics, Peking University People's Hospital, Beijing, China.
Missing data in electronic health records (EHRs) presents significant challenges in medical studies. Many methods have been proposed, but uncertainty exists regarding the current state of missing data addressing methods applied for EHR and which strategy performs better within specific contexts. All studies referencing EHR and missing data methods published from their inception until 2024 March 30 were searched via the MEDLINE, EMBASE, and Digital Bibliography and Library Project databases.
View Article and Find Full Text PDFAppropriately Matching Transport Care Units to Patients in Interhospital Transport Care: Implementation Study.
JMIR Form Res
December 2024
Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States.
Background: In interfacility transport care, a critical challenge exists in accurately matching ambulance response levels to patients' needs, often hindered by limited access to essential patient data at the time of transport requests. Existing systems cannot integrate patient data from sending hospitals' electronic health records (EHRs) into the transfer request process, primarily due to privacy concerns, interoperability challenges, and the sensitive nature of EHR data. We introduce a distributed digital health platform, Interfacility Transport Care (ITC)-InfoChain, designed to solve this problem without compromising EHR security or data privacy.
View Article and Find Full Text PDFTree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.
JAMIA Open
December 2024
Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States.
Objectives: We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.
Materials And Methods: We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability.
Permissioned blockchain network for proactive access control to electronic health records.
BMC Med Inform Decis Mak
October 2024
Institute of Communications and Computer Systems, Iroon Polytechniou 9, 15780, Zografou, Greece.
Background: As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient's health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party.
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