AIMC Topic: Electronic Health Records
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Development of a natural language processing algorithm to detect chronic cough in electronic health records.
BMC pulmonary medicine
Jun 28, 2022
BACKGROUND: Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs fro...
A hybrid method based on semi-supervised learning for relation extraction in Chinese EMRs.
BMC medical informatics and decision making
Jun 27, 2022
BACKGROUND: Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification...
Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.
American journal of perinatology
Jun 25, 2022
OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record.
Ensemble Approaches to Recognize Protected Health Information in Radiology Reports.
Journal of digital imaging
Jun 17, 2022
Natural language processing (NLP) techniques for electronic health records have shown great potential to improve the quality of medical care. The text of radiology reports frequently constitutes a large fraction of EHR data, and can provide valuable ...
Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians' needs.
BMC psychiatry
Jun 17, 2022
BACKGROUND: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available cl...
Towards interpretable, medically grounded, EMR-based risk prediction models.
Scientific reports
Jun 15, 2022
Machine-learning based risk prediction models have the potential to improve patient outcomes by assessing risk more accurately than clinicians. Significant additional value lies in these models providing feedback about the factors that amplify an ind...
Machine Learning-Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance.
Journal of medical Internet research
Jun 7, 2022
BACKGROUND: Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clini...
The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review.
Yearbook of medical informatics
Jun 2, 2022
OBJECTIVES: The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectio...
Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees.
Journal of pediatric gastroenterology and nutrition
Jun 1, 2022
OBJECTIVE: To create a new methodology that has a single simple rule to identify height outliers in the electronic health records (EHR) of children.
Artificial intelligence in cardiology: fundamentals and applications.
Internal medicine journal
May 31, 2022
Artificial intelligence (AI) is an overarching term that encompasses a set of computational approaches that are trained through generalised learning to autonomously execute specific tasks. AI is a rapidly expanding field in medicine. In particular ca...
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