High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

Although freely accessible, it is provided in individual files for each year, and is difficult to combine in order to analyze spatial or temporal trends.

For instance, patients' diagnosis varies substantially across medical centers but collaboratively form long-tailed distributions for

By pre-training the model on domain-specific data, PubMED BERT is expected to have a better understanding of biomedical concepts, terminology, and language patterns compared to general domain models like BERT -base and BERT -large [ 95 ]. The main advantage of using PubMED BERT for biomedical text mining tasks is its domain-specific knowledge, which can lead to improved performance and more accurate results when fine-tuned on various downstream tasks, such as named entity recognition, relation extraction, document classification, and question answering. Since PubMED BERT is pre-trained on a large corpus of biomedical text, it is better suited to capturing the unique language patterns, complex terminology, and the relationships between entities in the biomedical domain.

We help address these challenges through three contributions. First, we publish a new dataset, EHRSHOT, which contains de-identified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine.

Dosing models often use differential equations to model biological dynamics.

In the United States, health insurance is a critical component of individuals' ability to access health care.