Semantic Cluster Labeling for Medical Relations

Alicante, Anita; Corazza, Anna; Isgrò, Francesco; Silvestri, Stefano

doi:10.1007/978-3-319-39687-3_18

Cited by 7 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their study explores the application of latent models, which helps us better understand the structure and language patterns in medical text. Alicante et al [4] conducted a study on textual features for medical records classification. Their work emphasizes the importance of textual features, which can guide us in selecting appropriate features for our research.…”

Section: Related Workmentioning

confidence: 99%

“…Their contribution is vital in comprehending the intricate structures and language patterns within medical text. Alicante et al [4] conducted a study on textual features for medical records classification, highlighting the significance of choosing appropriate textual features in research. Jindal and Taneja [5] adopted a lexical approach for text categorization of medical documents, offering a straightforward yet effective classification method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advancing Cancer Document Classification with R andom Forest

Che,

Hu,

Zhao

et al. 2023

AJST

View full text Add to dashboard Cite

In this study, we address the challenging task of biomedical text document classification of Cancer Doc Classification, specifically focusing on lengthy research papers related to cancer. Unlike previous research that often deals with shorter abstracts and concise summaries, we curated a unique dataset comprising documents with more extensive content, each exceeding 6 pages in length. To tackle this classification challenge, we employed the Random Forest Tree method. Random Forest is a powerful ensemble learning technique that combines multiple decision trees to enhance classification accuracy and robustness. It has been widely adopted in the field of machine learning and data science for its effectiveness in handling complex classification tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancing Cancer Document Classification with R andom Forest

Che,

Hu,

Zhao

et al. 2023

AJST

View full text Add to dashboard Cite

show abstract

“…The Word Embeddings (Mikolov et al, 2013) layer is a shallow neural network that maps the input text into a vector space. Previous experiments (see (Gargiulo et al, 2017b), (Gargiulo et al, 2017a), (Alicante et al, 2016b)) have shown the effectiveness of a Natural Language Processing (NLP) applied to input text before the training phase.…”

Section: Word Embeddings Modulementioning

confidence: 99%

Deep Convolution Neural Network for Extreme Multi-label Text Classification

Gargiulo¹,

Silvestri²,

Ciampi³

2018

Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies

Self Cite

View full text Add to dashboard Cite

In this paper we present an analysis on the usage of Deep Neural Networks for extreme multi-label and multiclass text classification. We will consider two network models: the first one is formed by a word embeddings (WEs) stage followed by two dense layers, hereinafter Dense, and a second model with a convolution stage between the WEs and the dense layers, hereinafter CNN-Dense. We will take into account classification problems characterized by different number of labels, ranging from an order of 10 to an order of 30, 000, showing the different performances of the neural networks varying the total label number and the average number of labels for sample, exploiting the hierarchical structure of the label space of the dataset used for experimental assessment. It is worth noting that multi-label classification is an harder problem if compared to multi-class, due to the variable number of labels associated to each sample. We will even investigate on the behaviour of the neural networks as function of the training hyperparameters, analysing the link between them and the dataset complexity. All the result will be evaluated using the PubMed scientific articles collection as test case.

show abstract