AIMC Topic: Electronic Health Records
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Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.
Nature communications
Jul 10, 2019
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform ...
Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records.
BMC medical informatics and decision making
Jul 9, 2019
BACKGROUND: Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. New approaches are therefore necessary ...
Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.
International journal of medical informatics
Jul 6, 2019
OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) al...
Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data.
Acta psychiatrica Scandinavica
Jul 6, 2019
OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first 3 days following admission could ...
A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data.
Journal of medical Internet research
Jul 5, 2019
BACKGROUND: The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to ident...
SuperOrder: Provider order recommendation system for outpatient clinics.
Health informatics journal
Jul 3, 2019
This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A t...
Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records.
JAMA network open
Jul 3, 2019
IMPORTANCE: Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of effort needed to use current methods might be mitigated by using routinely registered clinical notes.
HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.
Journal of biomedical informatics
Jun 27, 2019
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...
Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.
Scientific reports
Jun 25, 2019
Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural langua...
Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.
Pharmacoepidemiology and drug safety
Jun 19, 2019
PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.
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