AIMC Topic: Electronic Health Records

Clear Filters Showing 921 to 930 of 2670 articles

CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping algorithms and their execution in a high-throughput and reproducible manner. The objective of the stu...

Pseudo-data generation for the extraction of Problems, Treatments and Tests.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
One of the primary challenges for clinical Named Entity Recognition (NER) is the availability of annotated training data. Technical and legal hurdles prevent the creation and release of corpora related to electronic health records (EHRs). In this wor...

Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support service delivery and advance clinical and informatics research. Deep learning techniques have demonstrat...

Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Previous studies have shown that FHIR is capable of modeling both structured and unstructured data fro...

Automatic Classification of Electronic Nursing Narrative Records Based on Japanese Standard Terminology for Nursing.

Computers, informatics, nursing : CIN
In Japan, nursing records are not easily put to secondary use because nursing documentation is not standardized. In recent years, electronic health records have necessitated the creation of Japanese nursing terminology. The purpose of this study was ...

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.

Nature communications
Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and tr...

Testing the Use of Natural Language Processing Software and Content Analysis to Analyze Nursing Hand-off Text Data.

Computers, informatics, nursing : CIN
Natural language processing software programs are used primarily to mine both structured and unstructured data from the electronic health record and other healthcare databases. The mined data are used, for example, to identify vulnerable at-risk popu...

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Nature protocols
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron...

Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Clinical and translational science
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center ...