AIMC Topic: Electronic Health Records

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Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claim...

The Enterprise Imaging Value Proposition.

Journal of digital imaging
As resources in the healthcare environment continue to wane, leaders are seeking ways to continue to provide quality care bounded by the constraints of a reduced budget. This manuscript synthesizes the experience from a number of institutions to prov...

Big Data in Ophthalmology.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, give...

On stethoscopes, patient records, artificial intelligence and zettabytes: a glimpse into the future of digital medicine in Mexico.

Archivos de cardiologia de Mexico
Science and technology are modifying medicine at a dizzying pace. Although access in our country to the benefits of innovations in the area of devices, data storage and artificial intelligence is still very restricted, the advance of digital medicine...

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

Journal of biomedical informatics
Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usabili...

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

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reaching source for health research. Integrated EHR data can provide a larger sample size of the population...

Multilevel Self-Attention Model and its Use on Medical Risk Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Various deep learning models have been developed for different healthcare predictive tasks using Electronic Health Records and have shown promising performance. In these models, medical codes are often aggregated into visit representation without con...