AIMC Topic: Electronic Health Records
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Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency.
JAMA network open
Feb 3, 2025
IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.
A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.
BMC medical informatics and decision making
Feb 3, 2025
BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm fo...
Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.
Journal of medical systems
Feb 3, 2025
Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human...
NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review.
Journal of pain and symptom management
Jan 31, 2025
This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. It addresses gaps in existing literature by providing a broader perspective than previo...
Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction.
Scientific reports
Jan 31, 2025
Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essential to prevent the progression of this situation. However, many existing prediction systems failed to ...
Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.
The spine journal : official journal of the North American Spine Society
Jan 30, 2025
BACKGROUND CONTEXT: Preoperative risk assessment remains a challenge in spinal fusion operations. Predictive modeling provides data-driven estimates of postsurgical outcomes, guiding clinical decisions and improving patient care. Moreover, automated ...
Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.
Nature cancer
Jan 30, 2025
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-...
Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.
Parasites & vectors
Jan 29, 2025
BACKGROUND: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. Howeve...
Extracting Housing and Food Insecurity Information From Clinical Notes Using cTAKES.
Health services research
Jan 28, 2025
OBJECTIVE: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults.
EHR-ML: A data-driven framework for designing machine learning applications with electronic health records.
International journal of medical informatics
Jan 28, 2025
OBJECTIVE: The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key ob...
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