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Electronic Health Records

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Natural language processing for identification of refractory status epilepticus in children.

Epilepsia
OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic health records (EHRs) permits analysis of care approaches and disease outcomes at a lower cost than pro...

[Clinical application of large language models : Does ChatGPT replace medical report formulation? An experience report].

Innere Medizin (Heidelberg, Germany)
Artificial intelligence (AI)-based language models, such as ChatGPT offer an enormous potential for research and medical care but also for clinical workflow optimization by making medical documentation easier and more efficient in taking over standar...

Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of nega...

Negation and speculation processing: A study on cue-scope labelling and assertion classification in Spanish clinical text.

Artificial intelligence in medicine
Natural Language Processing (NLP) based on new deep learning technology is contributing to the emergence of powerful solutions that help healthcare providers and researchers discover valuable patterns within insurmountable volumes of health records a...

HealthNet: A Health Progression Network via Heterogeneous Medical Information Fusion.

IEEE transactions on neural networks and learning systems
Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients' health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predict...

Leveraging machine learning to create user-friendly models to mitigate appointment failure at dental school clinics.

Journal of dental education
PURPOSE/OBJECTIVES: This study had a twofold outcome. The first aim was to develop an efficient, machine learning (ML) model using data from a dental school clinic (DSC) electronic health record (EHR). This model identified patients with a high likel...

A deep learning approach for transgender and gender diverse patient identification in electronic health records.

Journal of biomedical informatics
BACKGROUND: Although accurate identification of gender identity in the electronic health record (EHR) is crucial for providing equitable health care, particularly for transgender and gender diverse (TGD) populations, it remains a challenging task due...

TraumaICD Bidirectional Encoder Representation From Transformers: A Natural Language Processing Algorithm to Extract Injury International Classification of Diseases, 10th Edition Diagnosis Code From Free Text.

Annals of surgery
OBJECTIVE: To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury International Classification of Diseases, 10th edition (ICD-10) diagnosis codes from trauma tertiary survey notes.

An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care.

Artificial intelligence in medicine
Deep Learning (DL) models have received increasing attention in the clinical setting, particularly in intensive care units (ICU). In this context, the interpretability of the outcomes estimated by the DL models is an essential step towards increasing...