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Found 25 papers, 14 papers with code
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition
Distantly-supervised named entity recognition (NER) locates and classifies entities using only knowledge bases and unlabeled corpus to mitigate the reliance on human-annotated labels.
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction
Electronic health records (EHRs) hold significant value for research and applications.
CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health
The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives.
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs
Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms struggle at clustering difficult terms and do not generalize well beyond UMLS terms.
High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models
Objective: To develop a high-throughput biomedical relation extraction system that takes advantage of the large language models'(LLMs) reading comprehension ability and biomedical world knowledge in a scalable and evidential manner.
The Internet of Responsibilities-Connecting Human Responsibilities using Big Data and Blockchain
Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management.
A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles
This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy.
EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models
Electronic health records (EHR) contain a wealth of biomedical information, serving as valuable resources for the development of precision medicine systems.
A Computationally Efficient Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles
The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints.
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
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Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations
Term clustering is important in biomedical knowledge graph construction.
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
Multimodal Learning on Graphs for Disease Relation Extraction
On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10. 0% (accuracy) and 17. 2% (F1-score) by fusing disease knowledge graphs with text information.
Semi-constraint Optimal Transport for Entity Alignment with Dangling Cases
To improve EA with dangling entities, we propose an unsupervised method called Semi-constraint Optimal Transport for Entity Alignment in Dangling cases (SoTead).
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
We construct a medical cross-lingual knowledge graph dataset, MedED, providing data for both the EA and DED tasks.
PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems
Results: PMC-Patients contains 167k patient summaries with 3. 1M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections.
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification
To address this issue, we devise an adaptive mechanism to align reinforcement learning and classification methods using distribution entropy as the medium.
Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation
Materials and Methods: Document-level translations are mixed to train bilingual word embeddings (BWEs) for the evaluation of cross-lingual word similarity, and sentence distance is defined by combining semantic and positional similarities of the sentences.
Biomedical Question Answering: A Survey of Approaches and Challenges
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots.
CODER: Knowledge infused cross-lingual medical term embedding for term normalization
This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation.
High-throughput relation extraction algorithm development associating knowledge articles and electronic health records
In this paper, we present Hi-RES, a framework for high-throughput relation extraction algorithm development.
Generation of Synthetic Electronic Medical Record Text
It can also be used as a data augmentation method to assist studies based on real EMR data.
Word Segmentation as Graph Partition
We propose a new approach to the Chinese word segmentation problem that considers the sentence as an undirected graph, whose nodes are the characters.
NILE: Fast Natural Language Processing for Electronic Health Records
This paper introduces the design and performance of Narrative Information Linear Extraction (NILE), a natural language processing (NLP) package for EHR analysis that we share with the medical informatics community.
