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Article ; Online: Social media insights into spatio-temporal emotional responses to COVID-19 crisis.

Wang, Siqi / Liang, Chao / Gao, Yunfan / Ye, Yu / Qiu, Jingyu / Tao, Chuang / Wang, Haofen

2024 Volume 85, Page(s) 103174

Abstract: The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time ... ...

Abstract	The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time geography and environmental psychology. This framework analyzes the distribution and trends of 6 categories of public sentiments in Shanghai during the COVID-19 crisis, considering the potential urban spatial influencing factors. The research specifically draws on social media data temporally coinciding with the spread of COVID-19 and the pre-trained language model RoBERTa-wwm-ext to classify public sentiment, in order to characterize the distribution and trends of dominant urban sentiment under the influence of epidemic at different phases. The interactions between urban geospatial features and sentiments are further modelled and explained using LightGBM algorithm and SHapley Additive exPlanations (SHAP) technique. The experimental findings reveal the subtle yet dynamic impact of the urban environment on the long-term spatial variation and trends of public sentiment under the epidemic, with green spaces and socio-economic status emerging as significant factors. Regions with higher permanent population consumption demonstrated more positive sentiments, underscoring the significance of socio-economic factors in urban planning and public health policy. This research offers the most extensive analysis to date on the influence of urban characteristics on public sentiment during Shanghai's epidemic life cycle also lays the groundwork for applying the STSM framework in future crises beyond COVID-19.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Social Media ; China/epidemiology ; Emotions ; Pandemics
Language	English
Publishing date	2024-01-18
Publishing country	England
Document type	Journal Article
ZDB-ID	1262540-1
ISSN	1873-2054 ; 1353-8292
ISSN (online)	1873-2054
ISSN	1353-8292
DOI	10.1016/j.healthplace.2024.103174
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Article ; Online: WDCIP

Siqi Wang / Xiaoxiao Zhao / Jingyu Qiu / Haofen Wang / Chuang Tao

Geo-spatial Information Science, Pp 1-

spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

2023 Volume 25

Abstract: ABSTRACTThe outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, ... ...

Abstract	ABSTRACTThe outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.
Keywords	COVID-19 ; spatio-temporal artificial intelligence ; epidemic prevention and control platform ; risk assessment ; SIR ; graph autoencoder ; Mathematical geography. Cartography ; GA1-1776 ; Geodesy ; QB275-343
Subject code	004
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Design and application of the intraoperative protective device for patients undergoing interventional therapy via the femoral artery approach.

Ju, Jieqin / Mao, Liqing / Wang, Yuejing / Xie, Haofen / Zhou, Shengjun

Technology and health care : official journal of the European Society for Engineering and Medicine

2023 Volume 32, Issue 2, Page(s) 831–840

Abstract: Background: Femoral artery puncture is still the most used surgical approach. Because the operation requires local anaesthesia, the patient may not be able to exert full self-control, and their upper and lower limbs and trunk need to be constrained by a ...

Abstract	Background: Femoral artery puncture is still the most used surgical approach. Because the operation requires local anaesthesia, the patient may not be able to exert full self-control, and their upper and lower limbs and trunk need to be constrained by a protection device. Objective: To explore the safe application effect of a new type of anti-movement protection device for upper and lower extremities, shoulders and chest in patients undergoing interventional therapy via the femoral artery approach. Methods: This is a prospective randomised controlled study. A total of 230 patients were randomly divided into two groups: the study group (n= 115) and the control group (n= 115). The time needed to implement the restraint operation and the loosening of the restraint device in the two groups was recorded, and the satisfaction of surgeons and nurses was investigated. Results: The time needed to perform restraint operation in the study group was significantly less than that in the control group (4.06 ± 0.61 min vs. 7.01 ± 0.76 min, P< 0.05). The satisfaction of surgeons and nurses with the use of the new protective device was significantly better than that of the conventional restraint band (P< 0.05). Conclusion: The new anti-movement protection device for upper and lower limbs, shoulders and chest can conveniently and quickly achieve effective protection and braking of patients, ensure the safety of surgery and improve satisfaction.
MeSH term(s)	Humans ; Femoral Artery/surgery ; Prospective Studies ; Protective Devices ; Research Design ; Lower Extremity ; Treatment Outcome
Language	English
Publishing date	2023-09-26
Publishing country	Netherlands
Document type	Randomized Controlled Trial ; Journal Article
ZDB-ID	1159961-3
ISSN	1878-7401 ; 0928-7329
ISSN (online)	1878-7401
ISSN	0928-7329
DOI	10.3233/THC-230254
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Article ; Online: Malnutrition accelerates the occurrence of infectious complications in patients with chronic kidney disease: A cross‐sectional survey of 682 patients with chronic kidney disease

Wang, Weihong / Dai, Lili / Ma, Jianwei / Gu, Lingna / Xie, Haofen / Fu, Jianfei

Nutrition in Clinical Practice. 2023 Oct., v. 38, no. 5 p.1167-1174

2023

Abstract: BACKGROUND: To investigate the influencing factors of infectious complications in patients with chronic kidney disease (CKD) and provide a basis for clinical diagnosis and prognosis evaluation of CKD. METHODS: A total of 682 patients with CKD were ... ...

Abstract	BACKGROUND: To investigate the influencing factors of infectious complications in patients with chronic kidney disease (CKD) and provide a basis for clinical diagnosis and prognosis evaluation of CKD. METHODS: A total of 682 patients with CKD were selected and divided into CKD stage 1–5 subgroups according to their glomerular filtration rate. Infectious complications, length of hospital stay, and total cost of hospitalization were recorded. The Global Leadership Initiative on Malnutrition (GLIM) diagnostic tool was used to assess the detection rate of malnutrition among patients. Univariate and multivariate analyses were performed in patients with and without infectious complications. RESULTS: The incidence rates of infectious complications in CKD stages 1–5 were 45.6%, 22.7%, 28.3%, 30.8%, and 40.4%, respectively. The overall detection rate of malnutrition among patients based on the GLIM criteria was 16.7%. The total detection rate of severe malnutrition was 14.2%, with all patients with severe malnutrition in CKD stages 3–5. The incidences of infectious complications in patients with and without malnutrition were 62.3% and 29%, respectively. Binary multivariate logistic regression analysis shows that malnutrition is a risk factor for infectious complications in patients with CKD, who are at 2.41 times higher risk than patients without malnutrition. There were significant differences in length of hospital stay and hospitalization costs between the patients with CKD with and without infectious complications (P < 0.01). CONCLUSION: Infectious complications are relatively common in patients with CKD. As CKD advances, the incidence of infectious complications increases. Moreover, malnutrition accelerates the occurrence of infectious complications in patients with CKD.
Keywords	cross-sectional studies ; diagnostic techniques ; glomerular filtration rate ; hospitals ; kidney diseases ; leadership ; malnutrition ; nutrition ; prognosis ; regression analysis ; risk factors
Language	English
Dates of publication	2023-10
Size	p. 1167-1174.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	645074-x
ISSN	1941-2452 ; 0884-5336
ISSN (online)	1941-2452
ISSN	0884-5336
DOI	10.1002/ncp.11040
Database	NAL-Catalogue (AGRICOLA)

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Zs.A 2451: Show issues

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Article ; Online: Malnutrition accelerates the occurrence of infectious complications in patients with chronic kidney disease: A cross-sectional survey of 682 patients with chronic kidney disease.

Wang, Weihong / Dai, Lili / Ma, Jianwei / Gu, Lingna / Xie, Haofen / Fu, Jianfei

Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition

2023 Volume 38, Issue 5, Page(s) 1167–1174

Abstract: Background: To investigate the influencing factors of infectious complications in patients with chronic kidney disease (CKD) and provide a basis for clinical diagnosis and prognosis evaluation of CKD.: Methods: A total of 682 patients with CKD were ... ...

Abstract	Background: To investigate the influencing factors of infectious complications in patients with chronic kidney disease (CKD) and provide a basis for clinical diagnosis and prognosis evaluation of CKD. Methods: A total of 682 patients with CKD were selected and divided into CKD stage 1-5 subgroups according to their glomerular filtration rate. Infectious complications, length of hospital stay, and total cost of hospitalization were recorded. The Global Leadership Initiative on Malnutrition (GLIM) diagnostic tool was used to assess the detection rate of malnutrition among patients. Univariate and multivariate analyses were performed in patients with and without infectious complications. Results: The incidence rates of infectious complications in CKD stages 1-5 were 45.6%, 22.7%, 28.3%, 30.8%, and 40.4%, respectively. The overall detection rate of malnutrition among patients based on the GLIM criteria was 16.7%. The total detection rate of severe malnutrition was 14.2%, with all patients with severe malnutrition in CKD stages 3-5. The incidences of infectious complications in patients with and without malnutrition were 62.3% and 29%, respectively. Binary multivariate logistic regression analysis shows that malnutrition is a risk factor for infectious complications in patients with CKD, who are at 2.41 times higher risk than patients without malnutrition. There were significant differences in length of hospital stay and hospitalization costs between the patients with CKD with and without infectious complications (P < 0.01). Conclusion: Infectious complications are relatively common in patients with CKD. As CKD advances, the incidence of infectious complications increases. Moreover, malnutrition accelerates the occurrence of infectious complications in patients with CKD.
MeSH term(s)	Humans ; Cross-Sectional Studies ; Renal Insufficiency, Chronic/complications ; Renal Insufficiency, Chronic/epidemiology ; Renal Insufficiency, Chronic/diagnosis ; Malnutrition/complications ; Malnutrition/diagnosis ; Malnutrition/epidemiology ; Length of Stay ; Hospitalization ; Nutrition Assessment ; Nutritional Status
Language	English
Publishing date	2023-07-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	645074-x
ISSN	1941-2452 ; 0884-5336
ISSN (online)	1941-2452
ISSN	0884-5336
DOI	10.1002/ncp.11040
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: KADEL

Tao, Wei / Zhou, Yucheng / Wang, Yanlin / Zhang, Hongyu / Wang, Haofen / Zhang, Wenqiang

Knowledge-Aware Denoising Learning for Commit Message Generation

2024

Abstract: Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a ... ...

Abstract	Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. Our source code and data are available at https://github.com/DeepSoftwareAnalytics/KADEL Comment: Accepted to ACM Transactions on Software Engineering and Methodology 2024 (TOSEM'24)
Keywords	Computer Science - Software Engineering ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2024-01-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Molecular Mechanism and Structure-activity Relationship of the Inhibition Effect between Monoamine Oxidase and Selegiline Analogues.

Chuanxi, Yang / Xiaoning, Wang / Chang, Gao / Yunxiang, Liu / Ziyi, Ma / Jinqiu, Zang / Haoce, Wang / Lin, Liu / Yonglin, Liu / Haofen, Sun / Weiliang, Wang

Current computer-aided drug design

2023

Abstract: Introduction: To investigate the inhibition properties and structure-activity relationship between monoamine oxidase (MAO) and selected monoamine oxidase inhibitors (MAOIs, including selegiline, rasagiline and clorgiline).: Methods: The inhibition ... ...

Abstract	Introduction: To investigate the inhibition properties and structure-activity relationship between monoamine oxidase (MAO) and selected monoamine oxidase inhibitors (MAOIs, including selegiline, rasagiline and clorgiline). Methods: The inhibition effect and molecular mechanism between MAO and MAOIs were identified via the half maximal inhibitory concentration (IC50) and molecular docking technology. Results: It was indicated that selegiline and rasagiline were MAO B inhibitors, but clorgiline was MAO-A inhibitor based on the selectivity index (SI) of MAOIs (0.000264, 0.0197 and 14607.143 for selegiline, rasagiline and clorgiline, respectively). The high-frequency amino acid residues of the MAOIs and MAO were Ser24, Arg51, Tyr69 and Tyr407 for MAO-A and Arg42 and Tyr435 for MAO B. The MAOIs and MAO A/B pharmacophores included the aromatic core, hydrogen bond acceptor, hydrogen bond donor-acceptor and hydrophobic core. Conclusion: This study shows the inhibition effect and molecular mechanism between MAO and MAOIs and provides valuable findings on the design and treatment of Alzheimer's and Parkinson's diseases.
Language	English
Publishing date	2023-05-03
Publishing country	United Arab Emirates
Document type	Journal Article
ISSN	1875-6697
ISSN (online)	1875-6697
DOI	10.2174/1573409919666230503143055
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding

Meng Wang / Haofen Wang / Xing Liu / Xinyu Ma / Beilun Wang

JMIR Medical Informatics, Vol 9, Iss 6, p e

Instrument Validation Study

2021 Volume 28277

Abstract: BackgroundMinimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a ... ...

Abstract	BackgroundMinimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions. ObjectiveLeveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity. MethodsWe propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. ResultsTo validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy. ConclusionsWe propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study.

Wang, Meng / Wang, Haofen / Liu, Xing / Ma, Xinyu / Wang, Beilun

JMIR medical informatics

2021 Volume 9, Issue 6, Page(s) e28277

Abstract: Background: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is ...

Abstract	Background: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions. Objective: Leveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity. Methods: We propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. Results: To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy. Conclusions: We propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by integrating drug knowledge graphs and biomedical text simultaneously in a common low-dimensional space. The model also predicts DDIs using multiple labels rather than single or binary labels. Extensive experiments were conducted on real-world data sets to demonstrate the effectiveness and efficiency of the model. The results show our proposed framework outperforms several state-of-the-art baselines.
Language	English
Publishing date	2021-06-24
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/28277
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications.

Wang, Haofen / Du, Huifang / Qi, Guilin / Chen, Huajun / Hu, Wei / Chen, Zhuo

JMIR medical informatics

2022 Volume 10, Issue 5, Page(s) e37215

Abstract: Background: With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a ... ...

Abstract	Background: With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. Objective: To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. Methods: In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain-specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. Results: OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. Conclusions: A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
Language	English
Publishing date	2022-05-13
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/37215
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