AIMC Topic: Electronic Health Records
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Improving Clinical Decision Making With a Two-Stage Recommender System.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses ch...
Advances of artificial intelligence in predicting frailty using real-world data: A scoping review.
Ageing research reviews
Oct 5, 2024
BACKGROUND: Frailty assessment is imperative for tailoring healthcare interventions for older adults, but its implementation remains challenging due to the effort and time needed. The advances of artificial intelligence (AI) and natural language proc...
Estimating the prevalence of select non-communicable diseases in Saudi Arabia using a population-based sample: econometric analysis with natural language processing.
Annals of Saudi medicine
Oct 3, 2024
BACKGROUND: Non-communicable diseases (NCDs) are a major public health challenge globally, including in Saudi Arabia. However, measuring the true extent of NCD prevalence has been hampered by a paucity of nationally representative epidemiological stu...
Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus.
Artificial intelligence in medicine
Oct 2, 2024
BACKGROUND: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions. However, they suffer...
MediAlbertina: An European Portuguese medical language model.
Computers in biology and medicine
Oct 2, 2024
BACKGROUND: Patient medical information often exists in unstructured text containing abbreviations and acronyms deemed essential to conserve time and space but posing challenges for automated interpretation. Leveraging the efficacy of Transformers in...
Toward High-Quality Real-World Laboratory Data in the Era of Healthcare Big Data.
Annals of laboratory medicine
Sep 30, 2024
With Industry 4.0, big data and artificial intelligence have become paramount in the field of medicine. Electronic health records, the primary source of medical data, are not collected for research purposes but represent real-world data; therefore, t...
Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.
Scientific reports
Sep 30, 2024
Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the elderly, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its m...
Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.
Journal of medical Internet research
Sep 30, 2024
BACKGROUND: Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach.
A large language model-based clinical decision support system for syncope recognition in the emergency department: A framework for clinical workflow integration.
European journal of internal medicine
Sep 28, 2024
Differentiation of syncope from transient loss of consciousness can be challenging in the emergency department (ED). Natural Language Processing (NLP) enables the analysis of free text in the electronic medical records (EMR). The present paper aimed ...
Development of a Predictive Hospitalization Model for Skilled Nursing Facility Patients.
Journal of the American Medical Directors Association
Sep 27, 2024
OBJECTIVES: Identifying skilled nursing facility (SNF) patients at risk for hospitalization or death is of interest to SNFs, patients, and patients' families because of quality measures, financial penalties, and limited clinical staffing. We aimed to...
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