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Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of medical service. However, the complexity of…
The increasingly complex and rapidly changing global health and socio-economic landscape requires fundamentally new ways of thinking, acting and collaborating to solve growing systems challenges. Cross-sectoral collaborations between…
Medical document analysis plays a crucial role in extracting essential clinical insights from unstructured healthcare records, supporting critical tasks such as differential diagnosis. Determining the most probable condition among…
The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare-industry. In many cases, patients need to share their EHR with healthcare professionals. Given the…
The imbalance between the supply and demand of healthcare resources is a global challenge, which is particularly severe in developing countries. Governments and academic communities have made various efforts to increase healthcare supply…
A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such…
Linear constrained optimization techniques have been applied to many real-world settings. In recent years, inferring the unknown parameters and functions inside an optimization model has also gained traction. This inference is often based…
Centralized electronic health record repositories are critical for advancing disease surveillance, public health research, and evidence-based policymaking. However, developing countries face persistent challenges in achieving this due to…
Disparities in access to healthcare have been well-documented in the United States, but their effects on electronic health record (EHR) data reliability and resulting clinical models are poorly understood. Using an All of Us dataset of…
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.…
Clinical documentation and data retrieval within Electronic Health Records (EHRs) contribute substantially to clinician workload and burnout. To address this, we developed Scout, an LLM-based EHR search and synthesis platform that enables…
Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application…
Background: As value-based care expands across the U.S. healthcare system, reducing health disparities has become a priority. Social determinants of health (SDoH) indices, like the widely used Area Deprivation Index (ADI), guide efforts to…
While scientists increasingly recognize the importance of metadata in describing their data, spreadsheets remain the preferred tool for supplying this information despite their limitations in ensuring compliance and quality. Various tools…
Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive,…
The increasing use of Electronic Health Records (EHR) has emphasized the need for standardization and interoperability in healthcare data management. The Ministry of Health and Family Welfare, Government of India, has introduced the…
Data-driven decisions shape public health policies and practice, yet persistent disparities in data representation skew insights and undermine interventions. To address this, we advance a structured roadmap that integrates public health…
The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical decision making. Structured EHRs contain longitudinal observations of patients across hospital…
Objectives: We propose a novel imputation method tailored for Electronic Health Records (EHRs) with structured and sporadic missingness. Such missingness frequently arises in the integration of heterogeneous EHR datasets for downstream…
The open data movement constitutes an approach to achieving accountability for government organizations, and is aligned with one of the sustainable development goals outlined by the United Nations. In the area of health care, government…