AIMC Topic: Electronic Health Records
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Automatic Classification of Electronic Nursing Narrative Records Based on Japanese Standard Terminology for Nursing.
Computers, informatics, nursing : CIN
May 12, 2021
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
May 11, 2021
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
May 10, 2021
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
May 5, 2021
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...
Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.
JAMA network open
May 3, 2021
IMPORTANCE: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.
Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.
Clinical and translational science
May 2, 2021
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 ...
Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.
Artificial intelligence in medicine
May 1, 2021
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that...
The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings.
Computers in biology and medicine
Apr 28, 2021
BACKGROUND: Word vectors or word embeddings are n-dimensional representations of words and form the backbone of Natural Language Processing of textual data. This research experiments with algorithms that augment word vectors with lexical constraints ...
Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.
Scientific reports
Apr 26, 2021
Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is ...
Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records.
Medical & biological engineering & computing
Apr 23, 2021
Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big data has a great potential to reduce the risk of CAD death. In this process, neural network (NN), as a ...
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