Search

Purpose: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. Methods: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. Results: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. Conclusion: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management. [ABSTRACT FROM AUTHOR]

Background: A proposal to encourage the preregistration of research on research integrity was developed and adopted as the Amsterdam Agenda at the 5th World Conference on Research Integrity (Amsterdam, 2017). This paper reports on the degree to which abstracts of the 6th World Conference in Research Integrity (Hong Kong, 2019) reported on preregistered research. Methods: Conference registration data on participants presenting a paper or a poster at 6th WCRI were made available to the research team. Because the data set was too small for inferential statistics this report is limited to a basic description of results and some recommendations that should be considered when taking further steps to improve preregistration. Results: 19% of the 308 presenters preregistered their research. Of the 56 usable cases, less than half provided information on the six key elements of the Amsterdam Agenda. Others provided information that invalidated their data, such as an uninformative URL. There was no discernable difference between qualitative and quantitative research. Conclusions: Some presenters at the WCRI have preregistered their research on research integrity, but further steps are needed to increase frequency and completeness of preregistration. One approach to increase preregistration would be to make it a requirement for research presented at the World Conferences on Research Integrity. [ABSTRACT FROM AUTHOR]

Background: Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper. Results: PubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively. Conclusions: By integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced. [ABSTRACT FROM AUTHOR]

Background: In recent years, with the development of artificial intelligence, the use of deep learning technology for clinical information extraction has become a new trend. Clinical Event Detection (CED) as its subtask has attracted the attention from academia and industry. However, directly applying the advancements in deep learning to CED task often yields unsatisfactory results. The main reasons are due to the following two points: (1) A great number of obscure professional terms in the electronic medical record leads to poor recognition performance of model. (2) The scarcity of datasets required for the task leads to poor model robustness. Therefore, it is urgent to solve these two problems to improve model performance.Methods: This paper proposes a combining data augmentation and domain information with TENER Model for Clinical Event Detection.Results: We use two evaluation metrics to compare the overall performance of the proposed model with the existing model on the 2012 i2b2 challenge dataset. Experimental results demonstrate that our proposed model achieves the best F1-score of 80.26%, type accuracy of 93% and Span F1-score of 90.33%, and outperforms the state-of-the-art approaches.Conclusions: This paper proposes a multi-granularity information fusion encoder-decoder framework, which applies the TENER model to the CED task for the first time. It uses the pre-trained language model (BioBERT) to generate word-level features, solving the problem of a great number of obscure professional terms in the electronic medical record lead to poor recognition performance of model. In addition, this paper proposes a new data augmentation method for sequence labeling tasks, solving the problem of the scarcity of datasets required for the task leads to poor model robustness. [ABSTRACT FROM AUTHOR]

Background: An increasing number of qualitative evidence syntheses papers are found in health care literature. Many of these syntheses use a strictly exhaustive search strategy to collect articles, mirroring the standard template developed by major review organizations such as the Cochrane and Campbell Collaboration. The hegemonic idea behind it is that non-comprehensive samples in systematic reviews may introduce selection bias. However, exhaustive sampling in a qualitative evidence synthesis has been questioned, and a more purposeful way of sampling papers has been proposed as an alternative, although there is a lack of transparency on how these purposeful sampling strategies might be applied to a qualitative evidence synthesis. We discuss in our paper why and how we used purposeful sampling in a qualitative evidence synthesis about 'sexual adjustment to a cancer trajectory', by giving a worked example.Methods: We have chosen a mixed purposeful sampling, combining three different strategies that we considered the most consistent with our research purpose: intensity sampling, maximum variation sampling and confirming/disconfirming case sampling.Results: The concept of purposeful sampling on the meta-level could not readily been borrowed from the logic applied in basic research projects. It also demands a considerable amount of flexibility, and is labour-intensive, which goes against the argument of many authors that using purposeful sampling provides a pragmatic solution or a short cut for researchers, compared with exhaustive sampling. Opportunities of purposeful sampling were the possible inclusion of new perspectives to the line-of-argument and the enhancement of the theoretical diversity of the papers being included, which could make the results more conceptually aligned with the synthesis purpose.Conclusions: This paper helps researchers to make decisions related to purposeful sampling in a more systematic and transparent way. Future research could confirm or disconfirm the hypothesis of conceptual enhancement by comparing the findings of a purposefully sampled qualitative evidence synthesis with those drawing on an exhaustive sample of the literature. [ABSTRACT FROM AUTHOR]

Background: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. Methods: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. Results: From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. Conclusion: The Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies. [ABSTRACT FROM AUTHOR]

Background: Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. Results: The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. Conclusion: The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts. [ABSTRACT FROM AUTHOR]

Despite the eradication of malaria across most European countries in the 1960s and 1970s, the anopheline vectors are still present. Most of the malaria cases that have been reported in Europe up to the present time have been infections acquired in endemic areas by travelers. However, the possibility of acquiring malaria by locally infected mosquitoes has been poorly investigated in Europe, despite autochthonous malaria cases having been occasionally reported in several European countries. Here we present an update on the occurrence of potential malaria vector species in Europe. Adopting a systematic review approach, we selected 288 papers published between 2000 and 2021 for inclusion in the review based on retrieval of accurate information on the following Anopheles species: An. atroparvus, An. hyrcanus sensu lato (s.l.), An. labranchiae, An. maculipennis sensu stricto (s.s.), An. messeae/daciae, An. sacharovi, An. superpictus and An. plumbeus. The distribution of these potential vector species across Europe is critically reviewed in relation to areas of major presence and principal bionomic features, including vector competence to Plasmodium. Additional information, such as geographical details, sampling approaches and species identification methods, are also reported. We compare the information on each species extracted from the most recent studies to comparable information reported from studies published in the early 2000s, with particular reference to the role of each species in malaria transmission before eradication. The picture that emerges from this review is that potential vector species are still widespread in Europe, with the largest diversity in the Mediterranean area, Italy in particular. Despite information on their vectorial capacity being fragmentary, the information retrieved suggests a re-definition of the relative importance of potential vector species, indicating An. hyrcanus s.l., An. labranchiae, An. plumbeus and An. sacharovi as potential vectors of higher importance, while An. messeae/daciae and An. maculipennis s.s. can be considered to be moderately important species. In contrast, An. atroparvus and An. superpictus should be considered as vectors of lower importance, particularly in relation to their low anthropophily. The presence of gaps in current knowledge of vectorial systems in Europe becomes evident in this review, not only in terms of vector competence but also in the definition of sampling approaches, highlighting the need for further research to adopt the appropriate surveillance system for each species. [ABSTRACT FROM AUTHOR]

Background: Text Matching (TM) is a fundamental task of natural language processing widely used in many application systems such as information retrieval, automatic question answering, machine translation, dialogue system, reading comprehension, etc. In recent years, a large number of deep learning neural networks have been applied to TM, and have refreshed benchmarks of TM repeatedly. Among the deep learning neural networks, convolutional neural network (CNN) is one of the most popular networks, which suffers from difficulties in dealing with small samples and keeping relative structures of features. In this paper, we propose a novel deep learning architecture based on capsule network for TM, called CapsTM, where capsule network is a new type of neural network architecture proposed to address some of the short comings of CNN and shows great potential in many tasks.Methods: CapsTM is a five-layer neural network, including an input layer, a representation layer, an aggregation layer, a capsule layer and a prediction layer. In CapsTM, two pieces of text are first individually converted into sequences of embeddings and are further transformed by a highway network in the input layer. Then, Bidirectional Long Short-Term Memory (BiLSTM) is used to represent each piece of text and attention-based interaction matrix is used to represent interactive information of the two pieces of text in the representation layer. Subsequently, the two kinds of representations are fused together by BiLSTM in the aggregation layer, and are further represented with capsules (vectors) in the capsule layer. Finally, the prediction layer is a connected network used for classification. CapsTM is an extension of ESIM by adding a capsule layer before the prediction layer.Results: We construct a corpus of Chinese medical question matching, which contains 36,360 question pairs. This corpus is randomly split into three parts: a training set of 32,360 question pairs, a development set of 2000 question pairs and a test set of 2000 question pairs. On this corpus, we conduct a series of experiments to evaluate the proposed CapsTM and compare it with other state-of-the-art methods. CapsTM achieves the highest F-score of 0.8666.Conclusion: The experimental results demonstrate that CapsTM is effective for Chinese medical question matching and outperforms other state-of-the-art methods for comparison. [ABSTRACT FROM AUTHOR]

Background: Systematic identification of evidence in health policy can be time-consuming and challenging. This study examines three questions pertaining to systematic reviews on obesity prevention policy, in order to identify the most efficient search methods: (1) What percentage of the primary studies selected for inclusion in the reviews originated in scholarly as opposed to gray literature? (2) How much of the primary scholarly literature in this topic area is indexed in PubMed/MEDLINE? (3) Which databases index the greatest number of primary studies not indexed in PubMed, and are these databases searched consistently across systematic reviews? Methods: We identified systematic reviews on obesity prevention policy and explored their search methods and citations. We determined the percentage of scholarly vs. gray literature cited, the most frequently cited journals, and whether each primary study was indexed in PubMed. We searched 21 databases for all primary study articles not indexed in PubMed to determine which database(s) indexed the highest number of these relevant articles. Results: In total, 21 systematic reviews were identified. Ten of the 21 systematic reviews reported searching gray literature, and 12 reviews ultimately included gray literature in their analyses. Scholarly articles accounted for 577 of the 649 total primary study papers. Of these, 495 (76%) were indexed in PubMed. Google Scholar retrieved the highest number of the remaining 82 non-PubMed scholarly articles, followed by Scopus and EconLit. The Journal of the American Dietetic Association was the most-cited journal. Conclusions: Researchers can maximize search efficiency by searching a small yet targeted selection of both scholarly and gray literature resources. A highly sensitive search of PubMed and those databases that index the greatest number of relevant articles not indexed in PubMed, namely multidisciplinary and economics databases, could save considerable time and effort. When combined with a gray literature search and additional search methods, including cited reference searching and consulting with experts, this approach could help maintain broad retrieval of relevant studies while improving search efficiency. Findings also have implications for designing specialized databases for public health research. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing biomedical resources will provide support to the research on the hepatocellular carcinoma. To this purpose, we constructed a knowledge graph for Hepatocellular Carcinoma (KGHC).Methods: We propose an approach to build a knowledge graph for hepatocellular carcinoma. Specifically, we first extracted knowledge from structured data and unstructured data. Since the extracted entities may contain some noise, we applied a biomedical information extraction system, named BioIE, to filter the data in KGHC. Then we introduced a fusion method which is used to fuse the extracted data. Finally, we stored the data into the Neo4j which can help researchers analyze the network of hepatocellular carcinoma.Results: KGHC contains 13,296 triples and provides the knowledge of hepatocellular carcinoma for healthcare professionals, making them free of digging into a large amount of biomedical literatures. This could hopefully improve the efficiency of researches on the hepatocellular carcinoma. KGHC is accessible free for academic research purpose at http://202.118.75.18:18895/browser/ .Conclusions: In this paper, we present a knowledge graph associated with hepatocellular carcinoma, which is constructed with vast amounts of structured and unstructured data. The evaluation results show that the data in KGHC is of high quality. [ABSTRACT FROM AUTHOR]

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis. [ABSTRACT FROM AUTHOR]

Background: Data cleaning is an important quality assurance in data linkage research studies. This paper presents the data cleaning and preparation process for a large-scale cross-jurisdictional Australian study (the Smoking MUMS Study) to evaluate the utilisation and safety of smoking cessation pharmacotherapies during pregnancy.Methods: Perinatal records for all deliveries (2003-2012) in the States of New South Wales (NSW) and Western Australia were linked to State-based data collections including hospital separation, emergency department and death data (mothers and babies) and congenital defect notifications (babies in NSW) by State-based data linkage units. A national data linkage unit linked pharmaceutical dispensing data for the mothers. All linkages were probabilistic. Twenty two steps assessed the uniqueness of records and consistency of items within and across data sources, resolved discrepancies in the linkages between units, and identified women having records in both States.Results: State-based linkages yielded a cohort of 783,471 mothers and 1,232,440 babies. Likely false positive links relating to 3703 mothers were identified. Corrections of baby's date of birth and age, and parity were made for 43,578 records while 1996 records were flagged as duplicates. Checks for the uniqueness of the matches between State and national linkages detected 3404 ID clusters, suggestive of missed links in the State linkages, and identified 1986 women who had records in both States.Conclusions: Analysis of content data can identify inaccurate links that cannot be detected by data linkage units that have access to personal identifiers only. Perinatal researchers are encouraged to adopt the methods presented to ensure quality and consistency among studies using linked administrative data. [ABSTRACT FROM AUTHOR]

Background: Healthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically.Methods: Utilizing the modern cloud-based technologies, we have developed a comprehensive system that includes multiple subsystems, from capturing MRs in clinics, to securely transferring MRs, storing and managing cloud-based MRs, to facilitating both machine learning and manual reading, and to performing iterative quality control before committing the semi-structured data into the desired database. To evaluate the reproducibility of extracted medical data elements by DRESS, we conduct a blinded reproducibility study, with 100 MRs from patients who have undergone surgical treatment of lung cancer in China. The study uses Kappa statistic to measure concordance of discrete variables, and uses correlation coefficient to measure reproducibility of continuous variables.Results: Using the DRESS, we have demonstrated the feasibility of extracting clinical data from unstructured MRs to create semi-structured and patient-centered electronic health record database. The reproducibility study with 100 patient's MRs has shown an overall high reproducibility of 98 %, and varies across six modules (pathology, Radio/chemo therapy, clinical examination, surgery information, medical image and general patient information).Conclusions: DRESS uses a double-reading, double-entry, and an independent adjudication, to manually curate structured data elements from unstructured clinical data. Further, through distributed computing strategies, DRESS protects data privacy by dividing MR data into de-identified modules. Finally, through internet-based computing cloud, DRESS enables many data specialists to work in a virtual environment to achieve the necessary scale of processing thousands MRs within days. This hybrid system represents probably a workable solution to solve the big medical data challenge. [ABSTRACT FROM AUTHOR]

Background: A study of pregnancy outcomes related to pregnancy in prison in New South Wales, Australia, designed a two stage linkage to add maternal history of incarceration and serious mental health morbidity, neonatal hospital admission and infant congenital anomaly diagnosis to birth data. Linkage was performed by a dedicated state-wide data linkage authority. This paper describes use of the linked data to determine pregnancy prison exposure pregnancy for a representative population of mothers.Methods: Researchers assessed the quality of linked records; resolved multiple-matched identities; transformed event-based incarceration records into person-based prisoner records and birth records into maternity records. Inconsistent or incomplete records were censored. Interrogation of the temporal relationships of all incarceration periods from the prisoner record with pregnancies from birth records identified prisoner maternities. Interrogation of maternities for each mother distinguished prisoner mothers who were incarcerated during pregnancy, from prisoner control mothers with pregnancies wholly in the community and a subset of prisoner mothers with maternities both types of maternity. Standard descriptive statistics are used to provide population prevalence of exposures and compare data quality across study populations stratified by mental health morbidity.Results: Women incarcerated between 1998 and 2006 accounted for less than 1 % of the 404,000 women who gave birth in NSW between 2000 and 2006, while women with serious mental health morbidity accounted for 7 % overall and 68 % of prisoners. Rates of false positive linkage were within the predicted limits set by the linkage authority for non-prisoners, but were tenfold higher among prisoners (RR 9.9; 95%CI 8.2, 11.9) and twice as high for women with serious mental health morbidity (RR 2.2; 95%CI 1.9, 2.6). This case series of 597 maternities for 558 prisoners pregnant while in prison (of whom 128 gave birth in prison); and 2,031 contemporaneous prisoner control mothers is one of the largest available.Conclusions: Record linkage, properly applied, offers the opportunity to extend knowledge about vulnerable populations not amenable to standard ascertainment. Dedicated linkage authorities now provide linked data for research. The data are not research ready. Perinatal exposures are time-critical and require expert processing to prepare the data for research. [ABSTRACT FROM AUTHOR]

Background: PubMed is a widely used database for scientists to find biomedical-related literature. Due to the complexity of the selected research subject and its interdisciplinary nature, as well as the exponential growth in the number of disparate pieces of biomedical literature, it is an overwhelming challenge for scientists to define the right search strategies and quickly locate all related information. Specialized subsets and groupings of controlled vocabularies, such as Medical Subject Headings (MeSH), can enhance information retrieval in specialized domains, such as stem cell research. There is a need to develop effective search strategies and convenient solutions for knowledge organization in stem cell research. The understanding of the interrelationships between these MeSH terms also facilitates the building of knowledge organization systems in related subject fields.Methods: This study collected empirical data for MeSH-related terms from stem cell literature and developed a novel approach that uses both automation and expert-selection to create a set of terms that supports enhanced retrieval. The selected MeSH terms were reconstructed into a classified thesaurus that can guide researchers towards a successful search and knowledge organization of stem cell literature.Results: First, 4253 MeSH terms were harvested from a sample of 5527 stem cell related research papers from the PubMed database. Next, unrelated terms were filtered out based on term frequency and specificity. Precision and recall measures were used to help identify additional valuable terms, which were mostly non-MeSH terms. The study identified 15 terms that specifically referred to stem cell research for information retrieval, which would yield a higher precision (97.7 %) and recall (94.4 %) rates in comparison to other approaches. In addition, 128 root MeSH terms were selected to conduct knowledge organization of stem cell research in categories of anatomy, disease, and others.Conclusions: This study presented a novel strategy and procedure to reengineer term selections of the MeSH thesaurus for literature retrieval and knowledge organization using stem cell research as a case. It could help scientists to select their own search terms and build up a thesaurus-based knowledge organization system in interested and interdisciplinary research subject areas. [ABSTRACT FROM AUTHOR]

Background: Suffix arrays and their variants are used widely for representing genomes in search applications. Enhanced suffix arrays (ESAs) provide fast search speed, but require large auxiliary data structures for storing longest common prefix and child interval information. We explore techniques for compressing ESAs to accelerate genomic search and reduce memory requirements. Results: We evaluate various bitpacking techniques that store integers in fewer than 32 bits each, as well as bytecoding methods that reserve a single byte per integer whenever possible. Our results on the fly, chicken, and human genomes show that bytecoding with an exception guide array is the fastest method for retrieving auxiliary information. Genomic searching can be further accelerated using a data structure called a discriminating character array, which reduces memory accesses to the suffix array and the genome string. Finally, integrating storage of the auxiliary and discriminating character arrays further speeds up genomic search. Conclusions: The combination of exception guide arrays, a discriminating character array, and integrated data storage provide a 2- to 3-fold increase in speed for genomic searching compared with using bytecoding alone, and is 20% faster and 40% more space-efficient than an uncompressed ESA. [ABSTRACT FROM AUTHOR]

Background: High throughput sequencing technologies have become fast and cheap in the past years. As a result, large-scale projects started to sequence tens to several thousands of genomes per species, producing a high number of sequences sampled from each genome. Such a highly redundant collection of very similar sequences is called a pangenome. It can be transformed into a set of sequences "colored" by the genomes to which they belong. A colored de Bruijn graph (C-DBG) extracts from the sequences all colored k-mers, strings of length k, and stores them in vertices. Results: In this paper, we present an alignment-free, reference-free and incremental data structure for storing a pan-genome as a C-DBG: the bloom filter trie (BFT). The data structure allows to store and compress a set of colored k-mers, and also to efficiently traverse the graph. Bloom filter trie was used to index and query different pangenome datasets. Compared to another state-of-the-art data structure, BFT was up to two times faster to build while using about the same amount of main memory. For querying k-mers, BFT was about 52-66 times faster while using about 5.5-14.3 times less memory. Conclusion: We present a novel succinct data structure called the Bloom Filter Trie for indexing a pan-genome as a colored de Bruijn graph. The trie stores k-mers and their colors based on a new representation of vertices that compress and index shared substrings. Vertices use basic data structures for lightweight substrings storage as well as Bloom filters for efficient trie and graph traversals. Experimental results prove better performance compared to another state-of-the-art data structure. [ABSTRACT FROM AUTHOR]

Background: Electronic administrative data exist in several domains which, if linked, are potentially useful for research. However, benefits from data linkage should be considered alongside risks such as the threat to privacy. Avon Longitudinal Study of Parents and Children (ALSPAC) is a birth cohort study. The Project to Enhance ALSPAC through Record Linkage (PEARL) was established to enrich the ALSPAC resource through linkage between ALSPAC participants and routine sources of health and social data. Qualitative research was incorporated in the PEARL study to examine participants' views about data linkage and inform approaches to information sharing. This paper focusses on issues of consent.Methods: Digitally recorded interviews were conducted with 55 participants aged 17-19 years. Terms and processes relating to consent, anonymization and data linkage were explained to interviewees. Scenarios were used to prompt consideration of linking different sources of data, and whether consent should be requested. Interview recordings were fully transcribed. Thematic analysis was undertaken using the Framework approach.Results: Participant views on data linkage appeared to be most influenced by: considerations around the social sensitivity of the research question, and; the possibility of tangible health benefits in the public interest. Some participants appeared unsure about the effectiveness of anonymization, or did not always view effective anonymization as making consent unnecessary. This was related to notions of ownership of personal information and etiquette around asking permission for secondary use. Despite different consent procedures being explained, participants tended to equate consent with 'opt-in' consent through which participants are 'asked' if their data can be used for a specific study. Participants raising similar concerns came to differing conclusions about whether consent was needed. Views changed when presented with different scenarios, and were sometimes inconsistent.Conclusions: Findings from this study question the validity of 'informed consent' as a cornerstone of good governance, and the extent to which potential research participants understand different types of consent and what they are consenting, or not consenting, to. Pragmatic, imaginative and flexible approaches are needed if research using data linkage is to successfully realise its potential for public good without undermining public trust in the research process. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve.Methods: We develop a standardized data analysis process to support cohort study with a focus on a particular disease. We use an interactive divide-and-conquer approach to classify patients into relatively uniform within each group. It is a repetitive process enabling the user to divide the data into homogeneous subsets that can be visually examined, compared, and refined. The final visualization was driven by the transformed data, and user feedback direct to the corresponding operators which completed the repetitive process. The output results are shown in a Sankey diagram-style timeline, which is a particular kind of flow diagram for showing factors' states and transitions over time.Results: This paper presented a visually rich, interactive web-based application, which could enable researchers to study any cohorts over time by using EMR data. The resulting visualizations help uncover hidden information in the data, compare differences between patient groups, determine critical factors that influence a particular disease, and help direct further analyses. We introduced and demonstrated this tool by using EMRs of 14,567 Chronic Kidney Disease (CKD) patients.Conclusions: We developed a visual mining system to support exploratory data analysis of multi-dimensional categorical EMR data. By using CKD as a model of disease, it was assembled by automated correlational analysis and human-curated visual evaluation. The visualization methods such as Sankey diagram can reveal useful knowledge about the particular disease cohort and the trajectories of the disease over time. [ABSTRACT FROM AUTHOR]

Background: One of the primary obstacles to the widespread adoption of openEHR methodology is the lack of practical persistence solutions for future-proof electronic health record (EHR) systems as described by the openEHR specifications. This paper presents an archetype relational mapping (ARM) persistence solution for the archetype-based EHR systems to support healthcare delivery in the clinical environment.Methods: First, the data requirements of the EHR systems are analysed and organized into archetype-friendly concepts. The Clinical Knowledge Manager (CKM) is queried for matching archetypes; when necessary, new archetypes are developed to reflect concepts that are not encompassed by existing archetypes. Next, a template is designed for each archetype to apply constraints related to the local EHR context. Finally, a set of rules is designed to map the archetypes to data tables and provide data persistence based on the relational database.Results: A comparison study was conducted to investigate the differences among the conventional database of an EHR system from a tertiary Class A hospital in China, the generated ARM database, and the Node + Path database. Five data-retrieving tests were designed based on clinical workflow to retrieve exams and laboratory tests. Additionally, two patient-searching tests were designed to identify patients who satisfy certain criteria. The ARM database achieved better performance than the conventional database in three of the five data-retrieving tests, but was less efficient in the remaining two tests. The time difference of query executions conducted by the ARM database and the conventional database is less than 130 %. The ARM database was approximately 6-50 times more efficient than the conventional database in the patient-searching tests, while the Node + Path database requires far more time than the other two databases to execute both the data-retrieving and the patient-searching tests.Conclusions: The ARM approach is capable of generating relational databases using archetypes and templates for archetype-based EHR systems, thus successfully adapting to changes in data requirements. ARM performance is similar to that of conventionally-designed EHR systems, and can be applied in a practical clinical environment. System components such as ARM can greatly facilitate the adoption of openEHR architecture within EHR systems. [ABSTRACT FROM AUTHOR]

Background: Many studies indicate that various health programmes have been failed because of the lack of appropriate information, education, and communication [IEC] for the target audiences. It is still unanswered which methods/means of communication could be the most powerful for changing behaviour or decision-making capacity. The paper aims to assess the effects of IEC on family enrolment in health insurance programme [HIP] in Nepal.Methods: We employed a household-based observational study with a control group. Altogether 810 household interviews were conducted in Baglung and Kailali districts of Nepal in 2018. The study used a validated structured interview schedule. Background characteristics of the family and respondents and their exposure to the means of communication were the independent variables while enrolment in health insurance [HI] was the dependent variable.Results: Data showed that 72% of the respondents heard about the HI and 66% knew the contribution amount for enrolment in HI. In the total enrolled households, 53% were household heads, 59% belonged to the age group 41-60 and 68% were above 60 years. More than half (56%) of rich compared to 46 and 49% of middle and poor (p < 0.05); 60% of the family member suffering from the chronic disease were enrolled in the HI. Similarly, 68% of those who heard about HI compared to 4 % who did not hear were enrolled (p < 0.001). A vast majority (69%) of those knowing contribution amount, 73% who interact with peer neighbour compared to 39% who did not, and 62% of those who listened to the radio and 63% of those who watched TV were enrolled in HI (p < 0.001). However, heard about HI (aOR = 21.18, 95%CI: 10.17-44.13, p < 0.001), knowledge about contribution amount (aOR = 5.13, 95%CI: 3.09-8.52, p < 0.001), having HI related books or guidelines (aOR = 4.84, 95%CI: 2.61-8.98, p < 0.001), and interact with peer or neighbours (aOR = 1.74, 95%CI: 1.34-2.65, p < 0.01) were appeared to be positive and significant predictors for enrolment in HI.Conclusion: Knowledge about HI and interaction with peers and neighbours about the HI scheme of the government could lead to higher participation in the HIP. It would be better to incorporate this strategy while planning interventions for increasing enrolment in the HIP. [ABSTRACT FROM AUTHOR]

Background: The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference. Results: This paper presents a lossless light-weight reference-based compression algorithm namely LW-FQZip to compress FASTQ data. The three components of any given input, i.e., metadata, short reads and quality score strings, are first parsed into three data streams in which the redundancy information are identified and eliminated independently. Particularly, well-designed incremental and run-length-limited encoding schemes are utilized to compress the metadata and quality score streams, respectively. To handle the short reads, LW-FQZip uses a novel light-weight mapping model to fast map them against external reference sequence(s) and produce concise alignment results for storage. The three processed data streams are then packed together with some general purpose compression algorithms like LZMA. LW-FQZip was evaluated on eight real-world NGS data sets and achieved compression ratios in the range of 0.111-0.201. This is comparable or superior to other state-of-the-art lossless NGS data compression algorithms. Conclusions: LW-FQZip is a program that enables efficient lossless FASTQ data compression. It contributes to the state of art applications for NGS data storage and transmission. LW-FQZip is freely available online at: http://csse.szu.edu.cn/staff/ zhuzx/LWFQZip. [ABSTRACT FROM AUTHOR]

Background: The use of activated carbon (AC) in oral hygiene products has gained significant interest; however, its potential benefits for oral health remain uncertain. This study aimed to conduct a scientometric analysis to examine the dynamicity, emerging patterns, and trends over time in scientific production concerning the use of AC in oral health. Methods: The Web of Science database was searched for articles published between 2005 and 2022. Various bibliometric indicators, including the H-index, annual growth, Lotka's law, Bradford's law, and Sankey diagram, were used for data analysis. Overlay maps, timezone visualization, and three field plots were used to evaluate visualization patterns, time–temporal relationships, and trends. Information retrieval process was performed on March 11, 2023. Results: The analysis revealed that only six studies constituted the top references with the highest number of citations in recent years, with Brooks' 2017 study demonstrating the most significant increase in citation. The dual-map overlay demonstrated a close citation relationship between cluster 4 (Molecular Biology Immunology) and the areas of Environmental, Toxicology, and Nutrition. The visualization graph of publication patterns indicated the journals that accumulated the highest number of citations during the study period. Conclusion: This scientometric study provides valuable insights into the use of AC in oral health and its impact on the field of dentistry. It determines the most productive journals, authors, and countries with the greatest influence. AC effectively removes pollutants and is gaining interest for use in dental effluent treatment. Thus, it may be a viable option for professionals. [ABSTRACT FROM AUTHOR]

Background: A variety of high-throughput analyses, such as transcriptome, proteome, and metabolome analysis, have been developed, producing unprecedented amounts of omics data. These studies generate large gene lists, of which the biological significance shall be deeply understood. However, manually interpreting these lists is difficult, especially for non-bioinformatics-savvy scientists. Results: We developed an R package and a corresponding web server—Genekitr, to assist biologists in exploring large gene sets. Genekitr comprises four modules: gene information retrieval, ID (identifier) conversion, enrichment analysis and publication-ready plotting. Currently, the information retrieval module can retrieve information on up to 23 attributes for genes of 317 organisms. The ID conversion module assists in ID-mapping of genes, probes, proteins, and aliases. The enrichment analysis module organizes 315 gene set libraries in different biological contexts by over-representation analysis and gene set enrichment analysis. The plotting module performs customizable and high-quality illustrations that can be used directly in presentations or publications. Conclusions: This web server tool will make bioinformatics more accessible to scientists who might not have programming expertise, allowing them to perform bioinformatics tasks without coding. [ABSTRACT FROM AUTHOR]

Background: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.Results: Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems.Conclusions: A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset. [ABSTRACT FROM AUTHOR]

Background: Search filters aid clinicians and academics to accurately locate literature. Despite this, there is no search filter or Medical Subject Headings (MeSH) term pertaining to paramedics. Therefore, the aim of this study was to create two filters to meet to different needs of paramedic clinicians and academics.Methods: We created a gold standard from a reference set, which we measured against single terms and search filters. The words and phrases used stemmed from selective exclusion of terms from the previously published Prehospital Search Filter 2.0 as well as a Delphi session with an expert panel of paramedic researchers. Independent authors deemed articles paramedic-relevant or not following an agreed definition. We measured sensitivity, specificity, accuracy and number needed to read (NNR).Results: We located 2102 articles of which 431 (20.5%) related to paramedics. The performance of single terms was on average of high specificity (97.1% (Standard Deviation 7.4%), but of poor sensitivity (12.0%, SD 18.7%). The NNR ranged from 1 to 8.6. The sensitivity-maximising search filter yielded 98.4% sensitivity, with a specificity of 74.3% and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and thus a NNR of 1.48.Conclusions: We have created the first two paramedic specific search filters, one optimised for sensitivity and one optimised for specificity. The sensitivity-maximising search filter yielded 98.4% sensitivity, and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and a NNR of 1.48. A paramedic MeSH term is needed. [ABSTRACT FROM AUTHOR]

When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand. [ABSTRACT FROM AUTHOR]

Background: Systematic reviews (SRs) of randomized controlled trials (RCTs) are considered one of the most reliable study types. Through a systematic and thorough literature search, researchers aim to collect all research relevant to their purpose. The selection of databases can be challenging and depend on the topic of interest. The Cochrane Handbook suggests searching at least the following three databases: Cochrane Library, MEDLINE, and EMBASE. However, this is not always sufficient for reviews on the musculoskeletal field in general. This study aimed to examine the frequency and choice of databases used by researchers in SRs of spinal manipulative therapy (SMT). Secondly, to analyze the RCTs included in the SRs to determine the optimal combination of databases needed to conduct efficient literature searches for SRs of SMT. Methods: SRs investigating the effect of SMT on any patient-reported outcome measure were identified through searches in PubMed and Epistemonikos (all entries till date of search February 25, 2022). For each SR, databases searched and included RCTs were collected. RCTs were searched individually in nine databases (Cochrane Library, MEDLINE/PubMed, EMBASE, Google Scholar, CINAHL, Web of Science, Index to Chiropractic Literature, PEDro, and AMED). Coverage rates were calculated using the number of retrieved RCTs by the database or combinations of databases divided by the total number of RCTs. Results: Eighty-five SRs published met the inclusion criteria, and 442 unique RCTs were retrieved. The most frequently searched database was MEDLINE/PubMed. Cochrane Library had the highest overall coverage rate and contained the third most unique RCTs. While a 100% retrieval was not possible, as 18 RCTs could not be retrieved in any of the nine databases, the combination of Cochrane Library, Google Scholar, and PEDro retrieved all possible RCTs with a combined coverage rate of 95.9%. Conclusions: For SRs on SMT, we recommend using the combination suggested by the Cochrane Handbook of Cochrane Library, MEDLINE/PubMed, Embase, and in addition, PEDro and Index to Chiropractic Literature. Google Scholar might be used additionally as a tool for searching gray literature and quality assurance. [ABSTRACT FROM AUTHOR]