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Electronic Health Records

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Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect pe...

Bolstering Advance Care Planning Measurement Using Natural Language Processing.

Journal of palliative medicine
Despite its growth as a clinical activity and research topic, the complex dynamic nature of advance care planning (ACP) has posed serious challenges for researchers hoping to quantitatively measure it. Methods for measurement have traditionally depen...

Extracting adverse drug events from clinical Notes: A systematic review of approaches used.

Journal of biomedical informatics
BACKGROUND: An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and p...

SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization.

Journal of biomedical informatics
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions re...

Potential merits and flaws of large language models in epilepsy care: A critical review.

Epilepsia
The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intell...

Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records.

American journal of ophthalmology
PURPOSE: Nearly all published ophthalmology-related Big Data studies rely exclusively on International Classification of Diseases (ICD) billing codes to identify patients with particular ocular conditions. However, inaccurate or nonspecific codes may...

A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data.

Scientific reports
The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the ai...

Validation of a Zero-shot Learning Natural Language Processing Tool to Facilitate Data Abstraction for Urologic Research.

European urology focus
BACKGROUND: Urologic research often requires data abstraction from unstructured text contained within the electronic health record. A number of natural language processing (NLP) tools have been developed to aid with this time-consuming task; however,...

Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unst...