AIMC Topic: Electronic Health Records
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A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.
International journal of nursing studies
May 9, 2024
BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their ...
Development, evaluation and validation of machine learning models to predict hospitalizations of patients with coronary artery disease within the next 12 months.
International journal of medical informatics
May 9, 2024
BACKGROUND: Improved survival of patients after acute coronary syndromes, population growth, and overall life expectancy rise have led to a significant increase in the proportion of patients with stable coronary artery disease (CAD), creating a signi...
Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.
Applied clinical informatics
May 7, 2024
BACKGROUND: Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.
Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.
ESC heart failure
May 3, 2024
AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. ...
Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.
Journal of medical Internet research
May 2, 2024
BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status.
A Scoping Review of Studies Using Artificial Intelligence Identifying Optimal Practice Patterns for Inpatients With Type 2 Diabetes That Lead to Positive Healthcare Outcomes.
Computers, informatics, nursing : CIN
May 1, 2024
The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, t...
Foundation Models, Generative AI, and Large Language Models: Essentials for Nursing.
Computers, informatics, nursing : CIN
May 1, 2024
We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative techno...
Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries.
Computers, informatics, nursing : CIN
May 1, 2024
The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approa...
Data-Centric Machine Learning in Nursing: A Concept Clarification.
Computers, informatics, nursing : CIN
May 1, 2024
The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelli...
Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.
The journal of allergy and clinical immunology. In practice
Apr 27, 2024
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.
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