AIMC Topic: Electronic Health Records
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Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.
BMJ health & care informatics
Jul 1, 2024
OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-repr...
Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.
BMC public health
Jun 28, 2024
BACKGROUND: Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While...
Can the Administrative Loads of Physicians be Alleviated by AI-Facilitated Clinical Documentation?
Journal of general internal medicine
Jun 27, 2024
BACKGROUND: Champions of AI-facilitated clinical documentation have suggested that the emergent technology may decrease the administrative loads of physicians, thereby reducing cognitive burden and forestalling burnout. Explorations of physicians' ex...
Data-driven prediction of continuous renal replacement therapy survival.
Nature communications
Jun 27, 2024
Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they wi...
Reshaping free-text radiology notes into structured reports with generative question answering transformers.
Artificial intelligence in medicine
Jun 26, 2024
BACKGROUND: Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the a...
Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.
Addiction (Abingdon, England)
Jun 24, 2024
BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective ...
Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.
BMC medical informatics and decision making
Jun 24, 2024
OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML).
Improving clinical abbreviation sense disambiguation using attention-based Bi-LSTM and hybrid balancing techniques in imbalanced datasets.
Journal of evaluation in clinical practice
Jun 21, 2024
RATIONALE: Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to cla...
Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients' data.
Clinics in dermatology
Jun 21, 2024
Psoriasis is an immune-mediated skin disease affecting approximately 3% of the global population. Proper management of this condition necessitates the assessment of the body surface area and the involvement of nails and joints. The integration of nat...
A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.
Applied clinical informatics
Jun 19, 2024
BACKGROUND:  Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of ...
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