AI Medical Compendium Topic:
Electronic Health Records

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A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

European journal of radiology
PURPOSE: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their ris...

Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data.

PloS one
Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application ...

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer.

International journal of radiation oncology, biology, physics
Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language p...

A Method for Generating Synthetic Electronic Medical Record Text.

IEEE/ACM transactions on computational biology and bioinformatics
Machine learning (ML) and Natural Language Processing (NLP) have achieved remarkable success in many fields and have brought new opportunities and high expectation in the analyses of medical data, of which the most common type is the massive free-tex...

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.

Nature communications
Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artifici...

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. ...

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time...

A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In the electronic health record, the majority of clinically relevant information is stored within clinical notes. Most clinical notes follow a set organizational structure composed of canonicalized section headers that facilitate clinical review and ...

AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consumer products. This paper describes the process of mapping free-text allergy information from an elect...

Timely and Efficient AI Insights on EHR: System Design.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review and find what they are looking for under the time constraints of the clinical setting. Although rec...