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Found 9 papers, 4 papers with code
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
In the U. S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient.
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability.
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable consequence of rationing.
A Comparative Study of Pretrained Language Models for Long Clinical Text
Objective: Clinical knowledge enriched transformer models (e. g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks.
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines.
Multimodal Machine Learning in Precision Health
Attempts to improve prediction and resemble the multimodal nature of clinical expert decision-making this has been met in the computational field of machine learning by a fusion of disparate data.
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
To overcome this, long sequence transformer models, e. g. Longformer and BigBird, were proposed with the idea of sparse attention mechanism to reduce the memory usage from quadratic to the sequence length to a linear scale.
Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients
On the 5, 783 sepsis patients identified by the Sepsis-3 criteria statistically significant performance decreases for mortality prediction were observed when applying the trained machine learning model on Asian and Hispanic patients.
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clinical report auto-generation.
