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Found 14 papers, 4 papers with code
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare
This study aims to guide language model selection by investigating: 1) the necessity of finetuning versus zero-shot usage, 2) the benefits of domain-adjacent versus generic pretrained models, 3) the value of further domain-specific pretraining, and 4) the continued relevance of Small Language Models (SLMs) compared to Large Language Models (LLMs) for specific tasks.
Leveraging Language Models for Automated Patient Record Linkage
Discussion: Fine-tuned language models achieved strong performance in patient record blocking and matching with minimal errors.
ELM: Ensemble of Language Models for Predicting Tumor Group from Pathology Reports
Population-based cancer registries (PBCRs) face a significant bottleneck in manually extracting data from unstructured pathology reports, a process crucial for tasks like tumor group assignment, which can consume 900 person-hours for approximately 100, 000 reports.
A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting
We present an audit mechanism for language models, with a focus on models deployed in the healthcare setting.
Differentially Private Ensemble Classifiers for Data Streams
Learning from continuous data streams via classification/regression is prevalent in many domains.
The Differentially Private Lottery Ticket Mechanism
We propose the differentially private lottery ticket mechanism (DPLTM).
Differentially Private Survival Function Estimation
Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator.
Winning Privately: The Differentially Private Lottery Ticket Mechanism
We propose the differentially private lottery ticket mechanism (DPLTM).
Recovering Loss to Followup Information Using Denoising Autoencoders
Loss to followup is a significant issue in healthcare and has serious consequences for a study's validity and cost.
MIDA: Multiple Imputation using Denoising Autoencoders
Missing data is a significant problem impacting all domains.
Detecting Adversarial Samples Using Density Ratio Estimates
Machine learning models, especially based on deep architectures are used in everyday applications ranging from self driving cars to medical diagnostics.
CrowdMI: Multiple Imputation via Crowdsourcing
Can humans impute missing data with similar proficiency as machines?
Classifier comparison using precision
New proposed models are often compared to state-of-the-art using statistical significance testing.
Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image analysis.
