AIMC Topic: Electronic Health Records

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From real-world electronic health record data to real-world results using artificial intelligence.

Annals of the rheumatic diseases Sep 23, 2022
With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures almost the complete variety...
Research Design Electronic Health Records Algorithms Artificial Intelligence Machine Learning Humans
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Development and validation of a novel model for characterizing migraine outcomes within real-world data.

The journal of headache and pain Sep 21, 2022
BACKGROUND: In disease areas with 'soft' outcomes (i.e., the subjective aspects of a medical condition or its management) such as migraine or depression, extraction and validation of real-world evidence (RWE) from electronic health records (EHRs) and...
Artificial Intelligence Humans Headache Electronic Health Records Migraine Disorders Algorithms
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Multimodal biomedical AI.

Nature medicine Sep 15, 2022
The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimod...
Pandemics Artificial Intelligence Privacy Electronic Health Records Humans
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Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients.

Genetics in medicine : official journal of the American College of Medical Genetics Sep 13, 2022
PURPOSE: The variable expressivity and multisystem features of Noonan syndrome (NS) make it difficult for patients to obtain a timely diagnosis. Genetic testing can confirm a diagnosis, but underdiagnosis is prevalent owing to a lack of recognition a...
Noonan Syndrome Deep Learning Genetic Testing Child Databases, Factual Electronic Health Records Humans
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Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow.

Methods of information in medicine Sep 12, 2022
BACKGROUND: Clinical procedures are often performed in outpatient clinics without prior scheduling at the administrative level, and documentation of the procedure often occurs solely in free-text clinical electronic notes. Natural language processing...
Software Electronic Health Records Electronics Documentation Workflow Natural Language Processing
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Natural Language Processing: Unlocking the Potential of Electronic Health Record Data to Support Transdiagnostic Psychiatric Research.

Biological psychiatry. Cognitive neuroscience and neuroimaging Sep 9, 2022
Electronic Health Records Humans Natural Language Processing
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FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.

Journal of biomedical informatics Sep 9, 2022
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...
Data Warehousing Pattern Recognition, Automated Artificial Intelligence Humans Delivery of Health Care Electronic Health Records
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Weakly Semi-supervised phenotyping using Electronic Health records.

Journal of biomedical informatics Sep 5, 2022
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...
Algorithms Logistic Models Electronic Health Records Supervised Machine Learning Phenotype
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Dual-level diagnostic feature learning with recurrent neural networks for treatment sequence recommendation.

Journal of biomedical informatics Aug 28, 2022
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...
Electronic Health Records Learning Neural Networks, Computer Humans Machine Learning
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OARD: Open annotations for rare diseases and their phenotypes based on real-world data.

American journal of human genetics Aug 22, 2022
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...
Electronic Health Records Phenotype Humans Rare Diseases Natural Language Processing
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