AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Electronic Health Records

Showing 321 to 330 of 2332 articles

Clear Filters

Prediction of intra-abdominal injury using natural language processing of electronic medical record data.

Surgery
BACKGROUND: This study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records.

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

Antimicrobial resistance and infection control
BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO p...

Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.

Gastroenterology
BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may o...

Nursing workload: use of artificial intelligence to develop a classifier model.

Revista latino-americana de enfermagem
OBJECTIVE: to describe the development of a predictive nursing workload classifier model, using artificial intelligence.

Using Artificial Intelligence in Electronic Health Record Systems to Mitigate Physician Burnout: A Roadmap.

Journal of healthcare management / American College of Healthcare Executives
Physician burnout, a significant problem in modern healthcare, adversely affects healthcare professionals and their organizations. This essay explores the potential of artificial intelligence (AI) to positively address this issue through its integrat...

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analy...

Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies.

IEEE journal of biomedical and health informatics
Despite the vast potential for insights and value present in Electronic Health Records (EHRs), it is challenging to fully leverage all the available information, particularly that contained in the free-text data written by clinicians describing the h...

Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.

BMJ health & care informatics
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
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
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...