AI Medical Compendium Topic:
Electronic Health Records

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FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.

Journal of biomedical informatics
BACKGROUND: Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by...

Weakly Semi-supervised phenotyping using Electronic Health records.

Journal of biomedical informatics
OBJECTIVE: Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR...

Dual-level diagnostic feature learning with recurrent neural networks for treatment sequence recommendation.

Journal of biomedical informatics
In recent years, the massive electronic medical records (EMRs) have supported the development of intelligent medical services such as treatment recommendations. However, existing treatment recommendations usually follow the traditional sequential rec...

OARD: Open annotations for rare diseases and their phenotypes based on real-world data.

American journal of human genetics
Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated w...

AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences.

Journal of biomedical informatics
Early detection of heart failure (HF) can provide patients with the opportunity for more timely intervention and better disease management, as well as efficient use of healthcare resources. Recent machine learning (ML) methods have shown promising pe...

Discovering monogenic patients with a confirmed molecular diagnosis in millions of clinical notes with MonoMiner.

Genetics in medicine : official journal of the American College of Medical Genetics
PURPOSE: Cohort building is a powerful foundation for improving clinical care, performing biomedical research, recruiting for clinical trials, and many other applications. We set out to build a cohort of all monogenic patients with a definitive causa...

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Journal of biomedical informatics
Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large ...

PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data.

Journal of biomedical informatics
Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, howev...

New Frontiers of Natural Language Processing in Surgery.

The American surgeon
The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of th...

Machine learning for risk stratification in kidney disease.

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate ri...