AIMC Topic: Electronic Health Records
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EHR coding with hybrid attention and features propagation on disease knowledge graph.
Artificial intelligence in medicine
Jun 18, 2024
And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first cons...
The Rosetta System: Lessons for rural Australian health care from successful implementation of a hospital-wide natural language processing system in metropolitan South Australia.
The Australian journal of rural health
Jun 18, 2024
Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.
JDR clinical and translational research
Jun 14, 2024
OBJECTIVES: To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.
Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.
ESC heart failure
Jun 14, 2024
AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes....
A pilot randomized trial of automatic, artificial intelligence-based vs manual, electronic medical record-based remote postpartum blood pressure monitoring.
American journal of obstetrics and gynecology
Jun 14, 2024
Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records.
Journal of biomedical informatics
Jun 14, 2024
BACKGROUND: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve ...
Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation.
World neurosurgery
Jun 13, 2024
OBJECTIVE: To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs).
Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.
Journal of biomedical informatics
Jun 13, 2024
OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory c...
End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.
BMC medical informatics and decision making
Jun 12, 2024
Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the model...
Digital twins: reimagining the future of cardiovascular risk prediction and personalised care.
Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
Jun 7, 2024
The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the potential to redefine cardiovascular risk prevention. Digital twinning combines vast amounts of data fr...
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