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Found 11 papers, 0 papers with code
Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation
We propose custom2vec, an algorithmic framework to customize graph embeddings by incorporating user preferences in training the embeddings.
Clinical Trial Information Extraction with BERT
Natural language processing (NLP) of clinical trial documents can be useful in new trial design.
A Scalable AI Approach for Clinical Trial Cohort Optimization
FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria.
COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data
Here we proposed an approach to analyze text classification methods based on the presence or absence of task-specific terms (and their synonyms) in the text.
Cascade Neural Ensemble for Identifying Scientifically Sound Articles
Conclusion: Pre-trained contextual encoder neural networks (e. g. SciBERT) perform better than the models studied previously and manually created search filters in filtering for scientifically sound relevant articles.
Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction
Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications.
Knowledge Guided Named Entity Recognition for BioMedical Text
In this work, we formulate the NER task as a multi-answer knowledge guided QA task (KGQA) which helps to predict entities only by assigning B, I and O tags without associating entity types with the tags.
Training Models to Extract Treatment Plans from Clinical Notes Using Contents of Sections with Headings
Methods: We used common variations of plan headings and rule-based heuristics to find plan sections with headings in clinical notes, and we extracted sentences from them and formed a noisy training data of plan sentences.
Using Contextual Representations for Suicide Risk Assessment from Internet Forums
Our system achieved the 2nd best performance of 0. 477 macro averaged F measure on Task A of the challenge.
Developing and Using Special-Purpose Lexicons for Cohort Selection from Clinical Notes
To help automate the process, National NLP Clinical Challenges (N2C2) conducted a shared challenge by defining 13 criteria for clinical trial cohort selection and by providing training and test datasets.
Scoring Disease-Medication Associations using Advanced NLP, Machine Learning, and Multiple Content Sources
In predicting positive associations, the stacked combination significantly outperformed the baseline (a distant semi-supervised method on large medical text), achieving F scores of 0. 75 versus 0. 55 on the pairs seen in the patient records, and F scores of 0. 69 and 0. 35 on unique pairs.
