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Found 5 papers, 1 papers with code
Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insights
This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs).
Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks
In addition, our framework showed a great generalizability potential to transfer learned knowledge from one institution to another, paving the way for future healthcare model pre-training across institutions.
Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods
Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest.
BIG-bench Machine Learning
Explainable artificial intelligence
+1
Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis
The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level.
Distributed representation of patients and its use for medical cost prediction
Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction.
