Information Technology

HDR UK supporting mobilising computable biomedical knowledge in the UK.

Computable biomedical knowledge (CBK) represents an evolving area of health informatics, with potential for rapid translational patient benefit. Health Data Research UK (HDR UK) is the national Institute for Health Data Science, whose aim is to unite the UK's health data to enable discoveries that improve people's lives. The three main components include the UK HDR Alliance of data custodians, com...

Generating sequential electronic health records using dual adversarial autoencoder.

OBJECTIVE: Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent visits, rather than on chronological clinical records. In this article, we aim to learn and synt...

Deriving A Novel Health Index Using A Large-Scale Population Based Electronic Health Record With Deep Networks.

Health indexes are useful tools for monitoring the health condition of a population and can be used to guide healthcare policy of governments. However...

A Semi-Automated Approach for Multilingual Terminology Matching: Mapping the French Version of the ICD-10 to the ICD-10 CM.

The aim of this study was to develop a simple method to map the French International Statistical Classification of Diseases and Related Health Problem...

The Impact of Specialized Corpora for Word Embeddings in Natural Langage Understanding.

Recent studies in the biomedical domain suggest that learning statistical word representations (static or contextualized word embeddings) on large cor...

The Personal Health Library: A Single Point of Secure Access to Patient Digital Health Information.

Traditionally, health data management has been EMR-based and mostly handled by health care providers. Mechanisms are needed to give patients more cont...

Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this...

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.

MOTIVATION: Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have bee...

[Artificial intelligence, robotics and digitalization in the concept of military surgery of the German medical service].

BACKGROUND: As in other areas, military surgery is being transformed by developments in artificial intelligence, robotics and digitalization. Although...

Glaucoma screening: where are we and where do we need to go?

PURPOSE OF REVIEW: Current recommendations for glaucoma screening are decidedly neutral. No studies have yet documented improved long-term outcomes fo...

On classifying sepsis heterogeneity in the ICU: insight using machine learning.

OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of t...

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniatu...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well...

The Enterprise Imaging Value Proposition.

As resources in the healthcare environment continue to wane, leaders are seeking ways to continue to provide quality care bounded by the constraints o...

Ensuring electronic medical record simulation through better training, modeling, and evaluation.

OBJECTIVE: Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide sc...

Integrating blockchain technology with artificial intelligence for cardiovascular medicine.

Artificial intelligence (AI) holds promise for cardiovascular medicine but is limited by a lack of large, heterogeneous and granular data sets. Blockc...

Robust-ODAL: Learning from heterogeneous health systems without sharing patient-level data.

Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reachin...

Deep learning for electronic health records: A comparative review of multiple deep neural architectures.

Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digita...

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven p...

Structured reviews for data and knowledge-driven research.

UNLABELLED: Hypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hy...