相关论文: Review on Master Patient Index

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific…

Electronic medical records (EMR) contain sensitive personal information. For example, they may include details about infectious diseases, such as human immunodeficiency virus (HIV), or they may contain information about a mental illness.…

Individual entities are being connected every day with the advancement of Internet of Things (IoT). IoT contains various application domains and healthcare is one of them indeed. It is receiving a lot of attention recently because of its…

Current architectures to validate, certify, and manage identity are based on centralised, top-down approaches that rely on trusted authorities and third-party operators. We approach the problem of digital identity starting from a human…

We introduce a novel profile-based patient clustering model designed for clinical data in healthcare. By utilizing a method grounded on constrained low-rank approximation, our model takes advantage of patients' clinical data and digital…

In recent years, availability and usage of extensive systems for Electronic Healthcare Record (EHR) is increased. In medical centers such hospitals and other laboratories, more health data sets were formed during the treatment process. In…

Conventional service design methods are valuable for improving healthcare experience, but are limited in scale and information capture. Based on a constructed database of 2,320 stories from patients and carers with multiple long-term…

Designing patient-collected health data visualizations to support discussing patient data during clinical visits is a challenging problem due to the heterogeneity of the parties involved: patients, healthcare providers, and healthcare…

The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of thousands of critical care admissions, enabling a broad…

Healthcare data is a valuable resource for research, analysis, and decision-making in the medical field. However, healthcare data is often fragmented and distributed across various sources, making it challenging to combine and analyze…

Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public…

Healthcare information systems deal with a large amount of Personally Identifiable Information related to patients like dates of birth and social security numbers, patients health information and history, and financial information like…

Patient-controlled data-sharing systems are increasingly promoted as a way to empower patients with greater autonomy over their health data. Yet it remains unclear how different stakeholders, especially patients and health system leaders,…

Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods. This issue…

Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely…

Very large volumes of spatial data increasingly become available and demand effective management. While there has been decades of research on spatial data management, few works consider the current state of commodity hardware, having…

Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that…

The amount of data in the world is expanding rapidly. Every day, huge amounts of data are created by scientific experiments, companies, and end users' activities. These large data sets have been labeled as "Big Data", and their storage,…

Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental…

Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for…