AI Medical Compendium Topic

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

Electronic Health Records

Showing 391 to 400 of 2332 articles

Clear Filters

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Clinical research in cardiology : official journal of the German Cardiac Society
BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations.

Artificial intelligence in medicine
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational effic...

[Orthopedics and trauma surgery in the digital age].

Orthopadie (Heidelberg, Germany)
BACKGROUND: Digital transformation is shaping the future of orthopedics and trauma surgery. Telemedicine, digital health applications, electronic patient records and artificial intelligence play a central role in this. These technologies have the pot...

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of ris...

Using machine learning models to predict falls in hospitalised adults.

International journal of medical informatics
BACKGROUND: Identifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challe...

Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline.

Artificial intelligence in medicine
Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes...

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

Journal of biomedical informatics
OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation...

De-identification of clinical free text using natural language processing: A systematic review of current approaches.

Artificial intelligence in medicine
BACKGROUND: Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the p...

Practical use case of natural language processing for observational clinical research data retrieval from electronic health records: AssistMED project.

Polish archives of internal medicine
INTRODUCTION: Electronic health records (EHRs) contain data valuable for clinical research. However, they are in textual format and require manual encoding to databases, which is a lengthy and costly process. Natural language processing (NLP) is a co...