The ability of large language models (LLMs) to follow natural language
i...
External validation is often recommended to ensure the generalizability ...
Despite growing interest in using large language models (LLMs) in health...
The successes of foundation models such as ChatGPT and AlphaFold have sp...
Machine learning (ML) applications in healthcare are extensively researc...
While it has been well known in the ML community that deep learning mode...
Objective: There are several efforts to re-learn the 2013 ACC/AHA pooled...
A growing body of work uses the paradigm of algorithmic fairness to fram...
Predictive models for clinical outcomes that are accurate on average in ...
Motivation: Recognizing named entities (NER) and their associated attrib...
The use of machine learning to guide clinical decision making has the
po...
Informing clinical practice in a learning health system requires good ca...
Widespread adoption of electronic health records (EHRs) has fueled
devel...
The use of machine learning systems to support decision making in health...
Post-market medical device surveillance is a challenge facing manufactur...
Objectives: Most cancer data sources lack information on metastatic
recu...
Identifying patients who will be discharged within 24 hours can improve
...
Guidelines for the management of atherosclerotic cardiovascular disease
...
Electronic phenotyping, which is the task of ascertaining whether an
ind...
Personalized probabilistic forecasts of time to event (such as mortality...
Currently used clinical assessments for physical function do not objecti...
Predictive modeling with electronic health record (EHR) data is anticipa...
Improving the quality of end-of-life care for hospitalized patients is a...
When devising a course of treatment for a patient, doctors often have li...