AIMC Topic: Electronic Health Records
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Personalized Federated Graph Learning on Non-IID Electronic Health Records.
IEEE transactions on neural networks and learning systems
Sep 3, 2024
Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns ...
Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.
The Permanente journal
Sep 2, 2024
INTRODUCTION: Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is crucial because untreated psychosis is associated with poor outcomes and decreased treatment response. Lack of recognition of early psychotic symptoms...
Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.
Computer methods and programs in biomedicine
Aug 30, 2024
BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and system...
Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.
Sensors (Basel, Switzerland)
Aug 30, 2024
Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use diffe...
Special supplement issue on quality assurance and enrichment of biological and biomedical ontologies and terminologies.
BMC medical informatics and decision making
Aug 30, 2024
Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichme...
Identifying Elective Induction of Labor among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Health Care System.
American journal of perinatology
Aug 29, 2024
OBJECTIVE:  Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for researchers. We aimed to develop a Natural Language Processing (NLP) algorithm to identify eIOLs from el...
Multimodal fusion network for ICU patient outcome prediction.
Neural networks : the official journal of the International Neural Network Society
Aug 29, 2024
Over the past decades, massive Electronic Health Records (EHRs) have been accumulated in Intensive Care Unit (ICU) and many other healthcare scenarios. The rich and comprehensive information recorded presents an exceptional opportunity for patient ou...
The potential for large language models to transform cardiovascular medicine.
The Lancet. Digital health
Aug 29, 2024
Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early di...
From admission to discharge: a systematic review of clinical natural language processing along the patient journey.
BMC medical informatics and decision making
Aug 29, 2024
BACKGROUND: Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice...
A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.
PloS one
Aug 28, 2024
Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was ...
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