[PDF][PDF] Hierarchical Text Classification: a review of current research
A Zangari, M Marcuzzo, M Schiavinato… - EXPERT SYSTEMS …, 2023 - iris.unive.it
It is often the case that collections of documents are annotated with hierarchically-structured
concepts. However, the benefits of this structure are rarely taken into account by …
concepts. However, the benefits of this structure are rarely taken into account by …
Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study
Success rate of clinical trials (CTs) is low, with the protocol design itself being considered a
major risk factor. We aimed to investigate the use of deep learning methods to predict the …
major risk factor. We aimed to investigate the use of deep learning methods to predict the …
DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization
This paper presents the results of the Data Science for Digital Health (DS4DH) group in the
MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical …
MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical …
Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data
Several machine learning approaches have been proposed to automatically derive clinical
phenotypes from patient data. Nevertheless, methods leveraging similarity-based patient …
phenotypes from patient data. Nevertheless, methods leveraging similarity-based patient …
ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons
Clinical trials aim to study new tests and evaluate their effects on human health outcomes,
which has a huge market size. However, carrying out clinical trials is expensive and time …
which has a huge market size. However, carrying out clinical trials is expensive and time …
On graph construction for classification of clinical trials protocols using graph neural networks
A recent trend in health-related machine learning proposes the use of Graph Neural
Networks (GNN's) to model biomedical data. This is justified due to the complexity of …
Networks (GNN's) to model biomedical data. This is justified due to the complexity of …
Vaccine development in the time of COVID-19: the relevance of the Risklick AI to assist in risk assessment and optimize performance
The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration
of vaccine development worldwide. Rapid vaccine development poses numerous risks for …
of vaccine development worldwide. Rapid vaccine development poses numerous risks for …
Comparison of deep learning and model-based approaches for spatial profiling of the immune tumor environment on multiplex image data
E Ahmed - 2023 - diva-portal.org
The demographics of the tumor microenvironment (TME) impact the Immunotherapy
responses for lung cancer patients. Given the heterogeneity of immune cells present within …
responses for lung cancer patients. Given the heterogeneity of immune cells present within …