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Electronic Health Records

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Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.

ESC heart failure
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. ...

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
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
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
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
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
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BMJ health & care informatics
BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of mainte...

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.

BMC medical informatics and decision making
OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).

Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection.

International journal of antimicrobial agents
OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant Gram-negative infection...