bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring the efficacy of tumor necrosis factor alpha antagonists in the treatment of Ankylosing spondylarthritis: a pilot study based on MR relaxometry technique
Authors: Lin Mingui , Chen Xianyuan , Yu Shun , Gao Fei , Ma Mingping 
Published: July 2021

Abstract
Background
SpA is a disease that seriously affects the quality of life and working ability of patients. At present, there is a lack of scientific and effective quantitative indicators to evaluate the activity of sacroilitis and the efficacy of tumor necrosis factor-α antagonists in the treatment of active sacroilitis. MRI STIR sequence is the most commonly used method for the diagnosis of sacroiliac joint inflammation, but its response to the disease still lags behind the pathological changes and cannot provide quantitative indicators. This study aimed to evaluate the feasibility of using MRI Relaxometry technique to monitor the efficacy of TNF-α antagonists in the treatment of SpA, so as to provide an effective quantitative index for monitoring the efficacy.

Methods
This is a prospective study, 114 patients with sacroiliac joint were enrolled, including 15 patients as a control group, 99 patients as the case group, and 20 patients in the case group as the treatment group. The differences of T1 mapping, T2 mapping, T2* mapping of subchondral bone marrow of sacroiliac joint were compared among different groups. The diagnostic efficacy was analyzed by ROC, and the best quantitative index of diagnostic efficiency was used to monitor curative effects of different treatment cycles in the treatment group.

Results
1. Compared with the control group, values of three different relaxation times in the subchondral bone marrow region of the sacroiliac joint in the case group increased in varying degrees, and T1 mapping showed the best diagnostic efficacy. 2. The decreasing rate of T1 mapping in different treatment periods benefits the monitoring of curative effects.

Conclusion
This study indicates that T1 mapping technique is preferred in quantitative diagnosis. T1 mapping is superior to T2* mapping and T2 mapping in the diagnosis of subchondral BME of SpA. It can quantitatively monitor edema changes during treatment, benefiting clinical individualized treatment and timely adjustment of the treatment plan.
The impact of COVID-19 quarantine on dietary habits and physical activity in Saudi Arabia: a cross-sectional study
Authors: Bakhsh Manar Abduljalil , Khawandanah Jomana , Naaman Rouba Khalil , Alashmali Shoug 
Published: July 2021

Abstract
Background
The COVID-19 pandemic has forced governments around the world to impose strict hygiene and national lockdown measures, which in turn has changed the dietary and lifestyle habits of the world’s population. Thus, the aim of this study is to evaluate whether dietary and physical activity behaviors of Saudi Arabia’s adult population changed during the COVID-19 quarantine.

Methods
An electronic questionnaire which assessed changes in body weight, dietary habits, and physical activity of Saudi Arabia’s adult population (n = 2255) during the COVID-19 quarantine was distributed on social media between June and July 2020. To test the differences between changes in dietary and physical activity behaviors in relation to changes in body weight a Chi-square test was used.

Results
Over 40 and 45% of participants reported eating and snacking more, respectively, which led to weight gain in around 28%. Most participants reported that they consumed home-cooked (73%) and healthy meals (47%), while only 7% reported that they consumed foods from restaurants. Feelings of boredom and emptiness (44%) and the availability of time for preparing meals (40%) were the main reasons for changing dietary habits. Honey (43%) and vitamin C (50%) were the most consumed immune-boosting food and dietary supplement, respectively. COVID-19 also had a negative impact on physical activity, lowering the practice in 52% subjects, which was associated with significant weight gain (p < 0.001).

Conclusion
Assessing the changes to the population’s dietary habits and physical activity during the lockdown will help predict the outcome of the population’s future health and wellbeing after the pandemic.
The association between manual handling operations and pain in the hands and arms in the context of the 2018 BIBB/BAuA Employment Survey
Authors: Mueller Charlotte , Sauter Martha , Barthelme Julia , Liebers Falk 
Published: July 2021

Abstract
Background
Manual handling operations (MHO) are tasks performed by hand that require repetitive and forceful hand and arm movements. MHO are currently performed in many workplaces in skilled and unskilled jobs in the production and service sectors. MHO are considered as work-related health risk factors. The relationship between MHO and the occurrence of disorders of the upper extremities has been established. MHO can cause diseases such as tenosynovitis or carpal tunnel syndrome. This study aims to assess the current prevalence of MHO in the German workforce and to evaluate the relationship between MHO and the occurrence of hand and arm complaints.

Methods
The analysis was based on the German 2018 BIBB/BAuA Employment Survey. For this analysis we included subjects aged between 16 and 66 who work at least 35 h per week. The self-reported frequency of MHO (never; rarely; sometimes; often) was considered as the exposure of interest and was stratified by gender and occupation. Prevalence ratios (PR) were used to report the relationship between MHO and self-reported pain in the hands and arms (robust log-linear Poisson regression). Adjustments were made for age, gender, actual weekly working hours, psychosocial workload, and other physical workloads. The regression analyses considered complete cases.

Results
The analyses included 14,299 employees. Frequent MHO were reported by 32.6% of men and 31.1% of women. These workloads were often reported by respondents who work in the agricultural sector (men: 70.1%; women: 79.0%), in unskilled (men: 59.4%; women: 66.9%), and skilled manual occupations (men: 72.7%; women: 66.7%).
A higher frequency of self-reported MHO was associated with a higher prevalence of hand complaints (PR 2.26 CI 2.00–2.55 “often” vs. “never” = ref.) as well as arm pain (PR 1.73 CI 1.55–1.92 for “often” vs. “never” = ref.).

Conclusion
MHO are still frequent in many occupations. The shown association between MHO and pain in the hands and arms demonstrates the importance of MHO in the current German workforce and the necessity to further develop prevention strategies.
The use of Web-based interactive technology to promote HPV vaccine uptake among young females: a randomized controlled trial
Authors: Wang Qi , Zhang Wen 
Published: July 2021

Abstract
Background
Currently no study has investigated whether Web-based interactive technology can influence females to adopt healthy behaviors. We investigated how and under what conditions do Web-based interactivity influence vaccination intentions among young females.

Methods
In this randomized controlled trail, we conduct a 2 (mode of information presentation: narrative vs. data visualization) × 2 (interactivity: interactive information vs. noninteractive information) between-groups design. A total of 180 Chinese female undergraduate students who had never received HPV vaccination were randomly allocated to 4 experimental groups. Each participant was assessed for their information avoidance behavior and vaccination intention. The hypotheses were tested using a moderated mediation model. All analyses were performed using SPSS version 22.0 with probability set at 0.05 alpha level.

Results
The indirect relationship between interactivity and behavioral intention though information avoidance was moderated by the mode of presentation. Under the narrative condition, interactivity (vs. non-interactivity) decreased information avoidance and increased the intention to receive HPV vaccination (B = -.23, SE = 0.10, P < 0.05). However, under data visualization condition, no significant difference was observed between the effects of interactivity and non-interactivity on intention.

Conclusion
The findings suggest that when young females experience difficulties in manipulating or understanding HPV-related information, their information-avoidance behavior is likely to increase. Rather than use interactive statistical or graphical information, young females are more likely to be persuaded by interactive narratives.
Medical code prediction via capsule networks and ICD knowledge
Authors: Bao Weidong , Lin Hongfei , Zhang Yijia , Wang Jian , Zhang Shaowu 
Published: July 2021

Abstract
Background
Clinical notes record the health status, clinical manifestations and other detailed information of each patient. The International Classification of Diseases (ICD) codes are important labels for electronic health records. Automatic medical codes assignment to clinical notes through the deep learning model can not only improve work efficiency and accelerate the development of medical informatization but also facilitate the resolution of many issues related to medical insurance. Recently, neural network-based methods have been proposed for the automatic medical code assignment. However, in the medical field, clinical notes are usually long documents and contain many complex sentences, most of the current methods cannot effective in learning the representation of potential features from document text.

Methods
In this paper, we propose a hybrid capsule network model. Specifically, we use bi-directional LSTM (Bi-LSTM) with forwarding and backward directions to merge the information from both sides of the sequence. The label embedding framework embeds the text and labels together to leverage the label information. We then use a dynamic routing algorithm in the capsule network to extract valuable features for medical code prediction task.

Results
We applied our model to the task of automatic medical codes assignment to clinical notes and conducted a series of experiments based on MIMIC-III data. The experimental results show that our method achieves a micro F1-score of 67.5% on MIMIC-III dataset, which outperforms the other state-of-the-art methods.

Conclusions
The proposed model employed the dynamic routing algorithm and label embedding framework can effectively capture the important features across sentences. Both Capsule networks and domain knowledge are helpful for medical code prediction task.
The trend of direct medical costs and associated factors in patients with chronic hepatitis B in Guangzhou, China: an eight-year retrospective cohort study
Authors: Yang Shuo , Chen Ge , Li Yueping , Li Guanhai , Liang Yingfang , Zhou Feng , Zhou Shudong , Yang Yi , Jia Weidong , Gao Yanhui , Chen Yue 
Published: July 2021

Abstract
Background
Although the expenses of liver cirrhosis are covered by a critical illness fund under the current health insurance program in China, the medical costs associated with hepatitis B virus (HBV) related diseases is not well addressed. In order to provide evidence to address the problem, we investigated the trend of direct medical costs and associated factors in patients with chronic HBV infection.

Methods
A retrospective cohort study of 65,175 outpatients and 12,649 inpatients was conducted using a hospital information system database for the period from 2008 to 2015. Generalized estimating equations (GEE) were applied to explore associations between annual direct medical costs and corresponding factors, meanwhile quantile regression models were used to evaluate the effect of treatment modes on different quantiles of annual direct medical costs stratified by medical insurances.

Results
The direct medical costs increased with time, but the proportion of antiviral costs decreased with CHB progression. Antiviral costs accounted 54.61% of total direct medical costs for outpatients, but only 6.17% for inpatients. Non-antiviral medicine costs (46.06%) and lab tests costs (23.63%) accounted for the majority of the cost for inpatients. The direct medical costs were positively associated with CHB progression and hospitalization days in inpatients. The direct medical costs were the highest in outpatients with medical insurance and in inpatients with free medical service, and treatment modes had different effects on the direct medical costs in patients with and without medical insurance.

Conclusions
CHB patients had a heavy economic burden in Guangzhou, China, which increased over time, which were influenced by payment mode and treatment mode.
A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
Authors: Li Guixiang , Tan Zhongwei , Xu Weikang , Xu Fei , Wang Lei , Chen Jun , Wu Kai 
Published: July 2021

Abstract
Background
As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What’s more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease.

Methods
In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension.

Results
Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model.

Conclusion
In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
Transformers-sklearn: a toolkit for medical language understanding with transformer-based models
Authors: Yang Feihong , Wang Xuwen , Ma Hetong , Li Jiao 
Published: July 2021

Abstract
Background
Transformer is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of beginning to use transformer-based models in medical language understanding and expand the capability of the scikit-learn toolkit in deep learning, we proposed an easy to learn Python toolkit named transformers-sklearn. By wrapping the interfaces of transformers in only three functions (i.e., fit, score, and predict), transformers-sklearn combines the advantages of the transformers and scikit-learn toolkits.

Methods
In transformers-sklearn, three Python classes were implemented, namely, BERTologyClassifier for the classification task, BERTologyNERClassifier for the named entity recognition (NER) task, and BERTologyRegressor for the regression task. Each class contains three methods, i.e., fit for fine-tuning transformer-based models with the training dataset, score for evaluating the performance of the fine-tuned model, and predict for predicting the labels of the test dataset. transformers-sklearn is a user-friendly toolkit that (1) Is customizable via a few parameters (e.g., model_name_or_path and model_type), (2) Supports multilingual NLP tasks, and (3) Requires less coding. The input data format is automatically generated by transformers-sklearn with the annotated corpus. Newcomers only need to prepare the dataset. The model framework and training methods are predefined in transformers-sklearn.

Results
We collected four open-source medical language datasets, including TrialClassification for Chinese medical trial text multi label classification, BC5CDR for English biomedical text name entity recognition, DiabetesNER for Chinese diabetes entity recognition and BIOSSES for English biomedical sentence similarity estimation.
In the four medical NLP tasks, the average code size of our script is 45 lines/task, which is one-sixth the size of transformers’ script. The experimental results show that transformers-sklearn based on pretrained BERT models achieved macro F1 scores of 0.8225, 0.8703 and 0.6908, respectively, on the TrialClassification, BC5CDR and DiabetesNER tasks and a Pearson correlation of 0.8260 on the BIOSSES task, which is consistent with the results of transformers.

Conclusions
The proposed toolkit could help newcomers address medical language understanding tasks using the scikit-learn coding style easily. The code and tutorials of transformers-sklearn are available at https://doi.org/10.5281/zenodo.4453803. In future, more medical language understanding tasks will be supported to improve the applications of transformers_sklearn.
Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning
Authors: Zeng Kun , Xu Yibin , Lin Ge , Liang Likeng , Hao Tianyong 
Published: July 2021

Abstract
Background
Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data.

Methods
An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories.

Results
Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement.

Conclusions
A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.
Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients
Authors: Sengupta Kinshuk , Srivastava Praveen Ranjan 
Published: July 2021

Abstract
Background
In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice.

Methods
This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases.

Results
The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%.

Conclusion
The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.