Journal of Biomedical Semantics最新文献

{"title":"Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.","authors":"Amara Tariq,&nbsp;Omar Kallas,&nbsp;Patricia Balthazar,&nbsp;Scott Jeffery Lee,&nbsp;Terry Desser,&nbsp;Daniel Rubin,&nbsp;Judy Wawira Gichoya,&nbsp;Imon Banerjee","doi":"10.1186/s13326-022-00262-8","DOIUrl":"https://doi.org/10.1186/s13326-022-00262-8","url":null,"abstract":"<p><strong>Background: </strong>Transfer learning is a common practice in image classification with deep learning where the available data is often limited for training a complex model with millions of parameters. However, transferring language models requires special attention since cross-domain vocabularies (e.g. between two different modalities MR and US) do not always overlap as the pixel intensity range overlaps mostly for images.</p><p><strong>Method: </strong>We present a concept of similar domain adaptation where we transfer inter-institutional language models (context-dependent and context-independent) between two different modalities (ultrasound and MRI) to capture liver abnormalities.</p><p><strong>Results: </strong>We use MR and US screening exam reports for hepatocellular carcinoma as the use-case and apply the transfer language space strategy to automatically label imaging exams with and without structured template with > 0.9 average f1-score.</p><p><strong>Conclusion: </strong>We conclude that transfer learning along with fine-tuning the discriminative model is often more effective for performing shared targeted tasks than the training for a language space from scratch.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"8"},"PeriodicalIF":1.9,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39809029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multipurpose TNM stage ontology for cancer registries.","authors":"Nicholas Charles Nicholson,&nbsp;Francesco Giusti,&nbsp;Manola Bettio,&nbsp;Raquel Negrao Carvalho,&nbsp;Nadya Dimitrova,&nbsp;Tadeusz Dyba,&nbsp;Manuela Flego,&nbsp;Luciana Neamtiu,&nbsp;Giorgia Randi,&nbsp;Carmen Martos","doi":"10.1186/s13326-022-00260-w","DOIUrl":"https://doi.org/10.1186/s13326-022-00260-w","url":null,"abstract":"<p><strong>Background: </strong>Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions.</p><p><strong>Results: </strong>A unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task.</p><p><strong>Conclusions: </strong>The unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"7"},"PeriodicalIF":1.9,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39945025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending electronic medical records vector models with knowledge graphs to improve hospitalization prediction.","authors":"Raphaël Gazzotti,&nbsp;Catherine Faron,&nbsp;Fabien Gandon,&nbsp;Virginie Lacroix-Hugues,&nbsp;David Darmon","doi":"10.1186/s13326-022-00261-9","DOIUrl":"https://doi.org/10.1186/s13326-022-00261-9","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening their analysis and decisions, thereby improving the health of patients. On the one hand, machine learning algorithms have proven their effectiveness in extracting information and exploiting knowledge extracted from data. On the other hand, knowledge graphs capture human knowledge by relying on conceptual schemas and formalization and supporting reasoning. Leveraging knowledge graphs that are legion in the medical field, it is possible to pre-process and enrich data representation used by machine learning algorithms. Medical data standardization is an opportunity to jointly exploit the richness of knowledge graphs and the capabilities of machine learning algorithms.</p><p><strong>Methods: </strong>We propose to address the problem of hospitalization prediction for patients with an approach that enriches vector representation of EMRs with information extracted from different knowledge graphs before learning and predicting. In addition, we performed an automatic selection of features resulting from knowledge graphs to distinguish noisy ones from those that can benefit the decision making. We report the results of our experiments on the PRIMEGE PACA database that contains more than 600,000 consultations carried out by 17 general practitioners (GPs).</p><p><strong>Results: </strong>A statistical evaluation shows that our proposed approach improves hospitalization prediction. More precisely, injecting features extracted from cross-domain knowledge graphs in the vector representation of EMRs given as input to the prediction algorithm significantly increases the F1 score of the prediction.</p><p><strong>Conclusions: </strong>By injecting knowledge from recognized reference sources into the representation of EMRs, it is possible to significantly improve the prediction of medical events. Future work would be to evaluate the impact of a feature selection step coupled with a combination of features extracted from several knowledge graphs. A possible avenue is to study more hierarchical levels and properties related to concepts, as well as to integrate more semantic annotators to exploit unstructured data.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"6"},"PeriodicalIF":1.9,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39945027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text mining-based measurement of precision of polysomnographic reports as basis for intervention.","authors":"Florent Baty,&nbsp;Jemima Hegermann,&nbsp;Tiziana Locatelli,&nbsp;Claudio Rüegg,&nbsp;Christian Gysin,&nbsp;Frank Rassouli,&nbsp;Martin Brutsche","doi":"10.1186/s13326-022-00259-3","DOIUrl":"https://doi.org/10.1186/s13326-022-00259-3","url":null,"abstract":"<p><strong>Background: </strong>Text mining can be applied to automate knowledge extraction from unstructured data included in medical reports and generate quality indicators applicable for medical documentation. The primary objective of this study was to apply text mining methodology for the analysis of polysomnographic medical reports in order to quantify sources of variation - here the diagnostic precision vs. the inter-rater variability - in the work-up of sleep-disordered breathing. The secondary objective was to assess the impact of a text block standardization on the diagnostic precision of polysomnography reports in an independent test set.</p><p><strong>Results: </strong>Polysomnography reports of 243 laboratory-based overnight sleep investigations scored by 9 trained sleep specialists of the Sleep Center St. Gallen were analyzed using a text-mining methodology. Patterns in the usage of discriminating terms allowed for the characterization of type and severity of disease and inter-rater homogeneity. The variation introduced by the inter-rater (technician/physician) heterogeneity was found to be twice as high compared to the variation introduced by effective diagnostic information. A simple text block standardization could significantly reduce the inter-rater variability by 44%, enhance the predictive value and ultimately improve the diagnostic accuracy of polysomnography reports.</p><p><strong>Conclusions: </strong>Text mining was successfully used to assess and optimize the quality, as well as the precision and homogeneity of medical reporting of diagnostic procedures - here exemplified with sleep studies. Text mining methodology could lay the ground for objective and systematic qualitative assessment of medical reports.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"5"},"PeriodicalIF":1.9,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39576218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.","authors":"Bernd Müller,&nbsp;Leyla Jael Castro,&nbsp;Dietrich Rebholz-Schuhmann","doi":"10.1186/s13326-021-00258-w","DOIUrl":"https://doi.org/10.1186/s13326-021-00258-w","url":null,"abstract":"<p><strong>Background: </strong>Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly publications. Here we report the use of the Open Discovery Process on scientific literature to identify non-explicit ties between a disease, namely epilepsy, and known drugs, making full use of available epilepsy-specific ontologies.</p><p><strong>Results: </strong>We identified characteristics of epilepsy-specific ontologies to create subsets of documents from the literature; from these subsets we generated ranked lists of co-occurring neurological drug names with varying specificity. From these ranked lists, we observed a high intersection regarding reference lists of pharmaceutical compounds recommended for the treatment of epilepsy. Furthermore, we performed a drug set enrichment analysis, i.e. a novel scoring function using an adaptive tuning parameter and comparing top-k ranked lists taking into account the varying length and the current position in the list. We also provide an overview of the pharmaceutical space in the context of epilepsy, including a final combined ranked list of more than 70 drug names.</p><p><strong>Conclusions: </strong>Biomedical ontologies are a rich resource that can be combined with text mining for the identification of drug names for drug repurposing in the domain of epilepsy. The ranking of the drug names related to epilepsy provides benefits to patients and to researchers as it enables a quick evaluation of statistical evidence hidden in the scientific literature, useful to validate approaches in the drug discovery process.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"3"},"PeriodicalIF":1.9,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39945193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An annotated dataset for extracting gene-melanoma relations from scientific literature.","authors":"Roberto Zanoli,&nbsp;Alberto Lavelli,&nbsp;Theresa Löffler,&nbsp;Nicolas Andres Perez Gonzalez,&nbsp;Fabio Rinaldi","doi":"10.1186/s13326-021-00251-3","DOIUrl":"https://doi.org/10.1186/s13326-021-00251-3","url":null,"abstract":"<p><strong>Background: </strong>Melanoma is one of the least common but the deadliest of skin cancers. This cancer begins when the genes of a cell suffer damage or fail, and identifying the genes involved in melanoma is crucial for understanding the melanoma tumorigenesis. Thousands of publications about human melanoma appear every year. However, while biological curation of data is costly and time-consuming, to date the application of machine learning for gene-melanoma relation extraction from text has been severely limited by the lack of annotated resources.</p><p><strong>Results: </strong>To overcome this lack of resources for melanoma, we have exploited the information of the Melanoma Gene Database (MGDB, a manually curated database of genes involved in human melanoma) to automatically build an annotated dataset of binary relations between gene and melanoma entities occurring in PubMed abstracts. The entities were automatically annotated by state-of-the-art text-mining tools. Their annotation includes both the mention text spans and normalized concept identifiers. The relations among the entities were annotated at concept- and mention-level. The concept-level annotation was produced using the information of the genes in MGDB to decide if a relation holds between a gene and melanoma concept in the whole abstract. The exploitability of this dataset was tested with both traditional machine learning, and neural network-based models like BERT. The models were then used to automatically extract gene-melanoma relations from the biomedical literature. Most of the current models use context-aware representations of the target entities to establish relations between them. To facilitate researchers in their experiments we generated a mention-level annotation in support to the concept-level annotation. The mention-level annotation was generated by automatically linking gene and melanoma mentions co-occurring within the sentences that in MGDB establish the association of the gene with melanoma.</p><p><strong>Conclusions: </strong>This paper presents a corpus containing gene-melanoma annotated relations. Additionally, it discusses experiments which show the usefulness of such a corpus for training a system capable of mining gene-melanoma relationships from the literature. Researchers can use the corpus to develop and compare their own models, and produce results which might be integrated with existing structured knowledge databases, which in turn might facilitate medical research.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"2"},"PeriodicalIF":1.9,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39832970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach.","authors":"Sheeba Samuel,&nbsp;Birgitta König-Ries","doi":"10.1186/s13326-021-00253-1","DOIUrl":"https://doi.org/10.1186/s13326-021-00253-1","url":null,"abstract":"<p><strong>Background: </strong>The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment's computational and non-computational processes.</p><p><strong>Results: </strong>We present the \"REPRODUCE-ME\" data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps.</p><p><strong>Conclusion: </strong>We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":"1"},"PeriodicalIF":1.9,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39903869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Steps towards a Semantics of Dance","authors":"P. Patel-Grosz, P. Grosz, T. Kelkar, A. Jensenius","doi":"10.1093/jos/ffac009","DOIUrl":"https://doi.org/10.1093/jos/ffac009","url":null,"abstract":"As formal theoretical linguistic methodology has matured, recent years have seen the advent of applying it to objects of study that transcend language, e.g., to the syntax and semantics of music (Lerdahl & Jackendoff 1983, Schlenker 2017a; see also Rebuschat et al. 2011). One of the aims of such extensions is to shed new light on how meaning is construed in a range of communicative systems. In this paper, we approach this goal by looking at narrative dance in the form of Bharatanatyam. We argue that a semantic approach to dance can be modeled closely after the formal semantics of visual narrative proposed by Abusch (2013, 2014, 2021). A central conclusion is that dance not only shares properties of other fundamentally human means of expression, such as visual narrative and music, but that it also exhibits similarities to sign languages and the gestures of non-signers (see, e.g., Schlenker 2020) in that it uses space to track individuals in a narrative and performatively portray the actions of those individuals. From the perspective of general human cognition, these conclusions corroborate the idea that linguistic investigations beyond language (see Patel-Grosz et al. forthcoming) can yield insights into the very nature of the human mind and of the communicative devices that it avails.","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"34 1","pages":"693-748"},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73098665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidentiality in Abductive Reasoning: Experimental Support for a Modal Analysis of Evidentials","authors":"Anastasia Smirnova","doi":"10.1093/jos/ffab013","DOIUrl":"https://doi.org/10.1093/jos/ffab013","url":null,"abstract":"","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"10 1","pages":"531-570"},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85772677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relative Tense without Existential Quantification and Before","authors":"Toshiyuki Ogihara","doi":"10.1093/jos/ffac013","DOIUrl":"https://doi.org/10.1093/jos/ffac013","url":null,"abstract":"","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"23 1","pages":"657-691"},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83077172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}