AI Medical Compendium Topic:
Electronic Health Records

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Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.

International journal of medical informatics
BACKGROUND: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an im...

An Accurate Deep Learning Model for Clinical Entity Recognition From Clinical Notes.

IEEE journal of biomedical and health informatics
The growing use of electronic health records in the medical domain results in generating a large amount of medical data that is stored in the form of clinical notes. These clinical notes are enriched with clinical entities like disease, treatment, te...

Explainable Uncertainty-Aware Convolutional Recurrent Neural Network for Irregular Medical Time Series.

IEEE transactions on neural networks and learning systems
Influenced by the dynamic changes in the severity of illness, patients usually take examinations in hospitals irregularly, producing a large volume of irregular medical time-series data. Performing diagnosis prediction from the irregular medical time...

Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology.

Methods of information in medicine
BACKGROUND: Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a compu...

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The c...

An updated, computable MEDication-Indication resource for biomedical research.

Scientific reports
The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for mode...

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.

Applied clinical informatics
A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that cli...

Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.

International journal of medical informatics
BACKGROUND: Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses asso...

Federated learning for predicting clinical outcomes in patients with COVID-19.

Nature medicine
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe ...

Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy.

Computer methods and programs in biomedicine
OBJECTIVE: There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acut...