Information Technology

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning.

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR), which are the important digital carriers for recording medical activities of patients. Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE. This study aims to improve the recognition accuracy of these two tasks...

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.

An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to ter...

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.

OBJECTIVE: Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science....

TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis.

The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel root...

Synthesizing electronic health records using improved generative adversarial networks.

OBJECTIVE: The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those ...

Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.

Mappings between ontologies enable reuse and interoperability of biomedical knowledge. The Radiology Gamuts Ontology (RGO)-an ontology of 16 918 disea...

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of resear...

Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling.

UNLABELLED: Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging ...

Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0).

INTRODUCTION: This work describes the Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) corpus and provides an overview of ...

Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by pote...

Cancer Phenotype Development: A Literature Review.

EHR-based, computable phenotypes can be leveraged by healthcare organizations and researchers to improve the cohort identification process. The abilit...

Data Profiling in Support of Entity Resolution of Multi-Institutional EHR Data.

Information Quality (IQ) is a core tenant of contemporary data management practices. Across many disciplines and industries, it has become a necessary...

The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.

Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is ...

Enabling semantic queries across federated bioinformatics databases.

MOTIVATION: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data availabl...

The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.

Sickle cell disease (SCD) is one of the most common monogenic diseases in humans with multiple phenotypic expressions that can manifest as both acute ...

Zerobot®: A Remote-controlled Robot for Needle Insertion in CT-guided Interventional Radiology Developed at Okayama University.

Since 2012, we have been developing a remote-controlled robotic system (Zerobot®) for needle insertion during computed tomography (CT)-guided interven...

Clinical Integration of Digital Solutions in Health Care: An Overview of the Current Landscape of Digital Technologies in Cancer Care.

Digital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of dom...

Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.

Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integ...

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

OBJECTIVE: To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning archit...

Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.

OBJECTIVE: Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into ...