Nighttime features derived from topic models for classification of patients with COPD

Spina, Gabriele; Casale, Pierluigi; Albert, Paul S.; Alison, Jennifer A.; Garcia‐Aymerich, Judith; Clarenbach, Christian F.; Costello, Richard W.; Hernandes, Nídia A.; Leüppi, Jörg D.; Mesquita, Rafael; Singh, Sally; Smeenk, Frank W.J.M.; Tal‐Singer, Ruth; Wouters, Emiel F.M.; Spruit, Martijn A.; Brinker, A.C. den

doi:10.1016/j.compbiomed.2021.104322

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…Based on the experimental results, we give the following discussion and point out the limitations in this study and the future direction. Compared with multimodal sleep data [ 49 ] and electromyography [ 50 ], lung radiomics features extracted from chest HRCT images are more suitable for COPD stage classification.…”

Section: Discussionmentioning

confidence: 99%

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

et al. 2022

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Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.

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Section: Discussionmentioning

confidence: 99%

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

et al. 2022

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“…To express various styles of clothing more comprehensively and accurately, topic model is capable of learning topics; in other words, weighted lists of words and documents, from large collections of text documents (Spina et al , 2021; Damir et al , 2021), is introduced to classify them according to certain feature by unsupervised machine learning. Therefore, based on topic model, this research models the product information and corresponds to each model result as a design topic to express various design elements more comprehensively and accurately.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We employed a topic model to construct a design topic, which was originally proposed for natural language processing. The topic model is capable of scanning a large and unstructured collection of documents to automatically cluster similar word patterns and infer topics that best imply a concept or an aspect, represented as a series of related words according to the conditional probabilities of these words (Spina et al , 2021; Luo et al , 2019). Therefore, disclosing the issue of information imbalance and discovering consumer attention from the perspective of design topic holds relevance and significant value for business promotion in fast fashion.…”

Section: Introductionmentioning

confidence: 99%

How to discover consumer attention to design topics of fast fashion: a topic modeling approach

Pan,

Li,

Luo

et al. 2023

JFMM

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PurposeIt is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.Design/methodology/approachTo address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.FindingsThe experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.Practical implicationsThe issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.Originality/valueThis study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

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“…A recent study by Liu, Lee & Lee (2020) explored the topic embeddings generated from LDA to classify the email data, specifically they improved the email text classification with LDA topic modeling by converting email text into topic features. In the medical domain, Spina et al (2021) proposed a method that extracts nigh time features from multisensory data by using LDA and classify COPD (chronic obstructive and pulmonary disease) disease patients, they regard LDA topic distributions as powerful predictors in classifying the data. In another approach, Li & Suzuki (2021) used LDA-based topic modeling document representation to fine-grained the word sense disambiguation, they proposed a Bag of sense model in which a document is a multiset of word senses and LDA topics word distributions mapped into senses.…”

Section: Related Workmentioning

confidence: 99%

Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions

Wahid

Shi

Gao

et al. 2021

PeerJ Computer Science

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In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features’ cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generated from that data. We explored how the representation based on topics affect the classification performance by applying supervised classification algorithms. Additionally, we did careful evaluation on two types of datasets and compared them with baseline approaches without topic distributions and other comparable methods. The results show that our framework performs significantly better in terms of classification performance compared to the baseline(without T2F) approaches and also yields improvement in terms of F1 score compared to other compared approaches.

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Nighttime features derived from topic models for classification of patients with COPD

Cited by 6 publications

References 34 publications

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

How to discover consumer attention to design topics of fast fashion: a topic modeling approach

Topic2features: a novel framework to classify noisy and sparse textual data using LDA topic distributions

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