AIMC Topic: Electronic Health Records
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Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning.
BMC medical informatics and decision making
Jan 28, 2025
INTRODUCTION: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.
BMC medical research methodology
Jan 28, 2025
BACKGROUND: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that ful...
A pediatric emergency prediction model using natural language process in the pediatric emergency department.
Scientific reports
Jan 28, 2025
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021...
Scalable information extraction from free text electronic health records using large language models.
BMC medical research methodology
Jan 28, 2025
BACKGROUND: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting...
Discontinuous named entities in clinical text: A systematic literature review.
Journal of biomedical informatics
Jan 23, 2025
OBJECTIVE: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identi...
Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems.
Circulation. Genomic and precision medicine
Jan 23, 2025
BACKGROUND: While universal screening for Lipoprotein(a) [Lp(a)] is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), a ...
Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.
International journal of medical informatics
Jan 22, 2025
BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique vulnerabilities and etiologies. However, existing approaches fail to fully utilize regional heterogen...
Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.
Computers in biology and medicine
Jan 21, 2025
Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these conditions is crucial for timely intervention and prevention of adverse outcomes. In this study, we...
National survey on data governance and digital surgery: Challenges and opportunities for surgeons in the era of artificial intelligence.
Cirugia espanola
Jan 21, 2025
INTRODUCTION: This study evaluates the knowledge of Spanish surgeons regarding data governance and Digital Surgery, their usage, errors, and training deficiencies, as well as differences in knowledge between those who perform robotic surgery and thos...
Large language models vs human for classifying clinical documents.
International journal of medical informatics
Jan 21, 2025
BACKGROUND: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and...
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