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Found 11 papers, 3 papers with code
Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs.
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances
Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research.
Structured prediction models for RNN based sequence labeling in clinical text
In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks.
Active Learning for New Domains in Natural Language Understanding
We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system.
Continual Domain-Tuning for Pretrained Language Models
Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be tuned for different domains (domain-tuning) by continuing the pre-training phase on a new target domain corpus.
Calibrating Structured Output Predictors for Natural Language Processing
Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements.
Towards Automatic Generation of Questions from Long Answers
Therefore, we propose a novel evaluation benchmark to assess the performance of existing AQG systems for long-text answers.
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy
Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation.
Membership Inference Attack Susceptibility of Clinical Language Models
We show that membership inference attacks on CLMs lead to non-trivial privacy leakages of up to 7%.
Object-Aware Cropping for Self-Supervised Learning
This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly likely to be present in random crops of the full image.
