Biomedical Documents Research Articles

Effects of mindfulness-based stress reduction on cancer-related fatigue in patients with breast cancer: a meta-analysis of randomized controlled trials.

Mindfulness-based stress reduction (MBSR) has been widely used for improving psychological symptoms and sleep quality in breast cancer patients and has a positive impact on posttraumatic growth and immunology. Moreover, MBSR is increasingly being used in cancer-related fatigue (CRF) intervention studies for breast cancer patients, but conflicting results also exist. This study aimed to evaluate the effect of MBSR on CRF in patients with breast cancer. A comprehensive computer search of the Pubmed, Cochrane Library, Embase, Web of Science, China Biomedical Document Service System, China Knowledge Infrastructure Engineering, Wanfang Data Knowledge Service Platform, and VIP databases was performed. Randomized controlled trials (RCTs) published before April 10, 2023, were identified. The primary outcome was cancer-related fatigue associated with breast cancer. Two researchers independently screened the studies, extracted the data, and evaluated the methodological quality of the studies according to the inclusion and exclusion criteria. The Meta-analysis of the outcome indicators was performed using STATA 16.0 software. A total of 13 studies were included, including 1992 patients (997 patients in the MBSR group and 1015 patients in the control group). Compared with conventional care, MBSR significantly alleviated the symptoms of CRF in breast cancer patients (SMD=-0.32, 95% CI [-0.42, -0.22], z=6.54, p<.01). Under the supervision of experts, the 8-week MBSR had a great influence on CRF, especially in the Asian population. MBSR is effective in the treatment of CRF induced by breast cancer, and no obvious adverse effects occur; thus, MBSR can be recommended as a beneficial adjuvant therapy for treating CRF in breast cancer patients. https://www.crd.york.ac.uk/prospero/, identifier CRD42021245365.

Boundary-Aware Dual Biaffine Model for Sequential Sentence Classification in Biomedical Documents.

Assigning appropriate rhetorical roles, such as "background," "intervention," and "outcome," to sentences in biomedical documents can streamline the process for physicians to locate evidence and resources for medical treatment and decision-making. While sequence labeling and span-based methods are frequently employed for this task, the former disregards a document's semantic structure, resulting in a lack of semantic coherence across continuous sentences. Span-based approaches, on the other hand, either necessitate the enumeration of all potential spans, which can be time-consuming, or may lead to the misclassification of sentences over extended spans. Consequently, an approach is required that models the semantic structure of documents explicitly and captures boundary information to achieve precise and effective sentence labeling in biomedical documents. To address these challenges, we propose a new approach, the boundary-aware dual biaffine model, which explicitly models the semantic structure of documents and incorporates boundary information via a dual biaffine layer. We introduce a dynamic programming algorithm to minimize missing labels and overlapping predictions, and achieve globally optimal decoding results. We evaluate our approach on three benchmark datasets, namely PubMed 20 k RCT, PubMed-PICO and NICTA-PIBOSO. The experimental results demonstrate that our approach outperforms strong baselines and achieves state-of-the-art performance on PubMed 20 k RCT and PubMed-PICO. Additionally, our method also achieves competitive results on NICTA-PIBOSO.

Comparison of Open Surgery Versus Minimally Invasive Surgery in Nonsevere Adult Degenerative Scoliosis: A Systematic Review and Meta-Analysis.

A systematic review and meta-analysis. This study aimed to evaluate the clinical efficacy of minimally invasive surgery (MIS) and open surgery in correcting ADS. Adult degenerative scoliosis (ADS) is a scoliosis secondary to degenerative changes in the intervertebral discs and facet joints in adults. Severe low back pain, radicular pain, and intermittent claudication are often present and require surgical treatment. PubMed, Embase, The Cochrane Library, China National Knowledge Infrastructure (CNKI) Database, Wanfang Data, Weipu Database, and China Biomedical Document Service System (CBM) were systematically searched for studies that focused on the clinical efficacy of minimally invasive surgery and open surgery to correct ADS. This meta-analysis included 11 studies, involving 1527 patients (581 in the MIS group and 946 in the open surgery group). Regarding surgery and outcome indicators, the operative time in the open surgery group was shorter, the MIS group had less intraoperative blood loss, shorter hospitalization time, and lower incidence of serious postoperative complications. In terms of imaging parameters, although there was no significant difference in Cobb angle improvement and sagittal balance, the open surgery group exhibited better lumbar lordosis improvement and pelvic tilt improvement. In terms of clinical scores, including changes in the ODI index and VAS scores for low back and leg pain, similar improvements were appreciated across both groups. In mild to moderate ADS, we found that the advantages of open surgery include greater improvement in lumbar lordosis and pelvic tilt angle and shorter operative time. The advantages of minimally invasive surgery are less intraoperative blood loss, shorter hospital stay, and fewer serious postoperative complications. There is no significant difference between the 2 surgical methods in terms of Cobb angle, clinical pain, and sagittal vertical axis improvement.

Safety and efficacy of enhanced recovery after surgery among patients undergoing percutaneous nephrolithotomy: protocol for a systematic review and meta-analysis

IntroductionEnhanced recovery after surgery is widely used in the perioperative period in the field of urology; however, it lacks comprehensive and systematic evidence supporting its efficacy and safety after percutaneous...

An Ensemble Semantic Textual Similarity Measure Based on Multiple Evidences for Biomedical Documents.

With the increasing volume of the published biomedical literature, the fast and effective retrieval of the literature on the sequence, structure, and function of biological entities is an essential task for the rapid development of biology and medicine. To capture the semantic information in biomedical literature more effectively when biomedical documents are clustered, we propose a new multi-evidence-based semantic text similarity calculation method. Two semantic similarities and one content similarity are used, in which two semantic similarities include MeSH-based semantic similarity and word embedding-based semantic similarity. To fuse three different similarities more effectively, after, respectively, calculating two semantic and one content similarities between biomedical documents, feedforward neural network is applied to integrate the two semantic similarities. Finally, weighted linear combination method is used to integrate the semantic and content similarities. To evaluate the effectiveness, the proposed method is compared with the existing basic methods, and the proposed method outperforms the existing related methods. Based on the proven results of this study, this method can be used not only in actual biological or medical experiments such as protein sequence or function analysis but also in biological and medical research fields, which will help to provide, use, and understand thematically consistent documents.

Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering.

The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant articles from the rapidly growing biomedical literature due to the unsupervised nature of document clustering. Moreover, empirical investigations demonstrated that traditional text clustering methods produce unsatisfactory results in terms of non-contextualized vector space representations because that neglect the semantic relationship between biomedical texts. Recently, pre-trained language models have emerged as successful in a wide range of natural language processing applications. In this paper, we propose the Gaussian Mixture Model-based efficient clustering framework that incorporates substantially pre-trained (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) BioBERT domain-specific language representations to enhance the clustering accuracy. Our proposed framework consists of main three phases. First, classic text pre-processing techniques are used biomedical document data, which crawled from the PubMed repository. Second, representative vectors are extracted from a pre-trained BioBERT language model for biomedical text mining. Third, we employ the Gaussian Mixture Model as a clustering algorithm, which allows us to assign labels for each biomedical document. In order to prove the efficiency of our proposed model, we conducted a comprehensive experimental analysis utilizing several clustering algorithms while combining diverse embedding techniques. Consequently, the experimental results show that the proposed model outperforms the benchmark models by reaching performance measures of Fowlkes mallows score, silhouette coefficient, adjusted rand index, Davies-Bouldin score of 0.7817, 0.3765, 0.4478, 1.6849, respectively. We expect the outcomes of this study will assist domain specialists in comprehending thematically cohesive documents in the healthcare field.

Similarity Identification of Large-scale Biomedical Documents using Cosine Similarity and Parallel Computing

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification process that automatically calculates in the PubMed application. The execution time of mesh heading is 15.447 seconds, and the timely execution of abstract is 74.191 seconds. The execution time of mesh heading is higher than abstract because abstract contains more words than mesh heading. This study has successfully identified the similarity between large-scale biomedical documents of the PubMed documents that implemented a cosine similarity algorithm. The result has shown that the cosine similarity of the mesh heading texts is higher than the abstract text in the form of a graph and table shown in the PubMed application. The cosine similarity is useful to measure the similarity between documents based on the TF*IDF calculation result.

Classification of Full Text Biomedical Documents: Sections Importance Assessment

The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.

Development proposals of Human Research Protection Program.

China's ethics committees alone are unable to meet the growing need for human participant protection. Several scandals in recent years indicate weaknesses in the protection of human participants in China. The aim of the study is to summarize the status and problems of human research protection program in China and to explore its establishment proposals at national and hospital levels. To conduct literature retrieval, Chinese National Knowledge Infrastructure, Chinese Biomedical Document Database, and English databases Web of Science and PubMed were searched; laws, guidelines, and regulations were also searched on web by Google and Chinese search engine Baidu. No data were collected from human participants, and ethical review was not required. There are problems for China's Human Research Protection Program, such as weak relevant legal systems, insufficient administrative supervision, and incompetent ethics committee capacities. To fully protect human participants, China should promote the development of Human Research Protection Program, which can formulate ethics-related laws, improve regulations for the protection of the safety and rights of human participants, strengthen supervision, and enforce compensation for human participants. Owing to the frequency with which human participants are recruited in hospitals in China, hospitals can utilize existing ethics committees and establish data and safety monitoring committees, quality control, fund and contract management, and conflict of interest management offices. As a growing program, it remains necessary to learn from the experience of developed countries with high ethics standards and reformulate them to fit China's conditions to explore potential future development. The program will also be an experience for other developing countries. Human Research Protection Program can strengthen communication and coordination among various hospital departments to effectively protect the rights and welfare of human participants.

Anti-angiogenic effect of tripterygium glycosides tablets in animal models of rheumatoid arthritis: A systematic review and meta-analysis

To explore and summarize the beneficial effects of a traditional Chinese medicine preparation, Tripterygium glycosides tablets (TGT), in rheumatoid arthritis (RA) animal models of neovascularization, and to provide a reference for future clinical applications and research on its pharmacologic mechanism. We searched the databases PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, VIP, Wan Fang and SinoMed (China Biomedical Document Service System) to identify studies of TGT with outcome indicators of angiogenesis-related factors that were published before April 2020. Subgroup analysis and meta-regression were performed for dosage and duration of TGT. Statistical tests and subgroup analysis were conducted using RevMan 5.3, and meta-regression and sensitivity analysis were conducted using STATA/SE 15.0. Fourteen studies of TGT in RA rats were included in this analysis. Treatment with TGT significantly reduces synovial microvessel density and the expression of vascular endothelial growth factor (VEGF), VEGF receptor 2, hypoxia inducible factor α, c-Fos, c-Jun, angiopoietin-1 and angiopoietin-2 compared with control groups (P < .05). Subgroup analysis did not show a significant association of the mRNA levels of VEGF in synovium, assessed using quantitative real-time PCR, with duration or dosage of TGT. Meta-regression analysis also indicated that the effects of dosage and duration were not significantly associated with differences in VEGF mRNA levels. Sensitivity analysis on VEGF mRNA levels did not fundamentally change the results. TGT can reduce synovial neovascularization by decreasing synovial microvessel density and expression of VEGF, VEGF receptor 2, hypoxia-inducible factor α, c-Fos, c-Jun, Ang-1 and Ang-2, thereby suppressing pannus formation and bone destruction in rat models of RA. Additional well-designed studies are required to confirm these findings.

Feature Weighting in Finding Feedback Documents for Query Expansion in Biomedical Document Retrieval

Finding good feedback documents for query expansion is a well-known problem in the field of information retrieval. This paper describes a novel approach for finding relevant documents for feedback in query expansion for biomedical document retrieval. The proposed approach relies on a small amount of human intervention to find good feedback documents and tries to learn the relation between query and documents in terms of usefullness of document for query expansion. This proposed approach uses an NLP-based feature weighting technique with classification and clustering method on the documents and identifies relevant documents for feedback. The documents are represented using term frequency and inverse document frequency (TF–IDF) features and these features are weighted according to the type of query and type of the terms. The experiments performed on CDS 2014, 2015 and 2016 datasets show that the feature weighting in finding feedback documents for query expansion approach gives good results as compared to the results of pseudo-relevance feedback, relevance feedback and the results of TF–IDF features without weighting.

Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents

Recent deep learning approaches have shown promising results for named entity recognition (NER). A reasonable assumption for training robust deep learning models is that a sufficient amount of high-quality annotated training data is available. However, in many real-world scenarios, labeled training data is scarcely present. In this paper we consider two use cases: generic entity extraction from financial and from biomedical documents. First, we have developed a character based model for NER in financial documents and a word and character based model with attention for NER in biomedical documents. Further, we have analyzed how transfer learning addresses the problem of limited training data in a target domain. We demonstrate through experiments that NER models trained on labeled data from a source domain can be used as base models and then be fine-tuned with few labeled data for recognition of different named entity classes in a target domain. We also witness an interest in language models to improve NER as a way of coping with limited labeled data. The current most successful language model is BERT. Because of its success in state-of-the-art models we integrate representations based on BERT in our biomedical NER model along with word and character information. The results are compared with a state-of-the-art model applied on a benchmarking biomedical corpus.

Systematic review and sequential analysis on treatment of herpes zoster pain mainly by fire needle therapy

To assess the therapeutic effect and safety of fire needle therapy in the treatment of herpes zoster pain. We collected randomized controlled trials about treatment of herpes zoster pain mainly by fire needle therapy (published from the date of establishment of each database to Dec 1 of 2018) from databases of CNKI, Wanfang, VIP, Chinese Biomedical Document Service System (SinoMed), PubMed, Embase, Cochrane Library by using key words of "fire needle" "burnt needle" "red-hot needle" "herpes zoster" "postherpetic neuralgia" and "herpetic neuralgia". Then, we conducted Meta-analysis and sequential analysis about the outcomes of studies met our inclusion criteria using RevMan5.3.5 and TSA0.9.10 Beta softwares and employed GRADE profiler 3.6.1 to grade the quality of evidence. A total of 25 studies including 2 024 patients were brought into the present analysis. Outcomes of the qualitative analysis indicated that the fire needle therapy has a higher effective rate than the western medicine group. Meta-analysis showed that after the treatment mainly with fire needle therapy, 1) the VAS score is evidently lower [WMD=-0.96, 95% CI(-1.22, -0.71), P<0.000 01], 2) the duration of 30% pain relief is obviously shorter [WMD=-1.82, 95% CI (-2.46, -1.18), P<0.000 01], 3) the duration of pain is evidently shorter {fire needle combined with electroacupuncture [WMD=-11.53, 95% CI(-14.57, -8.48), P<0.000 01]; fire needle combined with cupping [WMD=-4.75, 95% CI (-7.99, -1.51), P=0.004]; pure fire needle therapy [WMD=-1.82, 95% CI (-2.46, -1.18), P<0.000 01], and 4) the occurrence rate of post-herpetic neuralgia is considerably lower [RR=0.16, 95% CI (0.09, 0.30), P<0.000 01]. The occurrence and management of adverse events were not mentioned in all the included studies, suggesting no safety problem of the therapy, but the publication bias has not been taken seriously. In the treatment of herpes zoster pain, fire needle as the main treatment approach can significantly relieve pain, shorten the pain duration, and reduce the incidence of post-herpetic neuralgia.

Determining the Influence of Class Imbalance for the Triage of Biomedical Documents

Determining the Influence of Class Imbalance for the Triage of Biomedical Documents

A Study of Research on Methods of Automated Biomedical Document Classification using Topic Modeling and Deep Learning

본 연구는 LDA 토픽 모델과 딥 러닝을 적용한 단어 임베딩 기반의 Doc2Vec 기법을 활용하여 자질을 선정하고 자질집합의 크기와 종류 및 분류 알고리즘에 따른 분류 성능의 차이를 평가하였다. 또한 자질집합의 적절한 크기를 확인하고 문헌의 위치에 따라 종류를 다르게 구성하여 분류에 이용할 때 높은 성능을 나타내는 ...

Implementation of a High-Performance Answer Snippet Retrieval System Based on Multiple Ranking Models for Biomedical Documents

Implementation of a High-Performance Answer Snippet Retrieval System Based on Multiple Ranking Models for Biomedical Documents

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge*. Spanish-English Cross-language Case Study.

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic annotator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers.

A Concept Based Ontology Mapping Method for Effective Retrieval of Bio-Medical Documents

A Concept Based Ontology Mapping Method for Effective Retrieval of Bio-Medical Documents

An Efficient Ontology Based Concept Indexing and Clustering for Biomedical Documents

Conventional Document clustering techniques aim to group the documents into different semantic classes based on the cluster hypothesis. Most of the existing techniques are based on either single term keyword with its frequency analysis or phrase based approach using n-gram techniques of the document. Accurate clustering is infeasible in document clustering because of the curse of dimensionality due to the high dimensionality space of it. For the successful clustering of text documents, a two step process is proposed in this paper. This proposed method involves with concept based indexing with the domain ontology as background knowledge for concept extraction and clustering of documents. The results of the proposed method is compared with the traditional indexing technique, Latent Semantic Indexing (LSI). In order to prove the efficiency of the proposed technique, biomedical domain is chosen with MeSH ontology. The experimental results show that the proposed method outperforms traditional term-base method and LSI.

Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/outcome models in the UK's largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors' own group) who work in text processing for biomedicine and other areas. GATE is available online <1> under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis.