Researchers have identified gut microbiota that interact with brain regions associated with emotion and mood. Literature reviews of those associations rely on rigorous systematic approaches and labor-intensive investments. Here we explore how knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relationships among them, is incorporated into the emotion-probiotic relationship exploration work.We propose an end-to-end emotion-probiotics relationship exploration method with an integrated medical knowledge graph, which incorporates the text mining output of knowledge graph, concept reasoning and evidence classification. Specifically, a knowledge graph for probiotics is built based on a text-mining analysis of PubMed, and further used to retrieve triples of relationships with reasoning logistics. Then specific relationships are annotated and evidence levels are retrieved to form a new evidence-based emotion-probiotic knowledge graph.Based on the probiotics knowledge graph with 40,442,404 triples, totally 1453 PubMed articles were annotated in both the title level and abstract level, and the evidence levels were incorporated to the visualization of the explored emotion-probiotic relationships. Finally, we got 4131 evidenced emotion-probiotic associations.The evidence-based emotion-probiotic knowledge graph construction work demonstrates an effective reasoning based pipeline of relationship exploration. The annotated relationship associations are supposed be used to help researchers generate scientific hypotheses or create their own semantic graphs for their research interests. 

/pdf/exploring-relationship-between-emotion-and-probiotics-with-dkwmwjks.pdf

Exploring relationship between emotion and probiotics with knowledge graphs

Background In the past few decades, growing adoption of electronic health record (EHR) systems has made massive clinical narrative data available electronically. Natural language processing (NLP) technologies that can unlock information from narrative text have received great attention in the medical domain. Many clinical NLP methods and systems have been developed and showed promising results in various tasks. These methods and tools have also been successfully applied to facilitate clinical research, as well as to support healthcare applications. Recent advancements in artificial intelligence (AI), particularly deep learning-based neural networks, have achieved state-of-the-art performance on diverse NLP tasks in general domain, indicating great opportunities for solving real-world medical problems. At the same time, the amount of health information available online has exploded through use of social media, community forums, and health-related websites. These present additional challenges and opportunities for further development of new NLP methodologies and applications.

/pdf/editorial-the-second-international-workshop-on-health-2dt7kz6kkk.pdf

Editorial: The second international workshop on health natural language processing (HealthNLP 2019).

Several thesauri have been published in various domains, or in the same subject domain. This heterogeneity caused the significant incompatibility of transferring or sharing data among different systems and databases. Therefore, thesaurus integration is a solution for handling this incompatibility issue. To achieve interoperability between different thesauri, mapping systems have been developed for establishing equivalents between terms in different thesauri. However, there is still ambiguity in term semantics and hierarchical relations used in thesauri. The purpose of this paper is to reexamine the issues and challenges in vocabulary mapping and integration between different controlled vocabulary systems. The paper outlines the history of the study of vocabulary mapping efforts and suggests a way in which the emerging practices on semantic issues and mapping problems can be articulated.

pdf/vocabulary-integration-reexamined-4tvbtb0zst.pdf

Vocabulary Integration Reexamined

Objectives A thread is the most common information aggregation unit in a health forum, so effective thread recommendation is critical for improving the user experience in an online health community (OHC). This paper proposes an OHC thread recommendation method based on topic model and network embedding, which recommends threads to users by training a classifier and predicting user reply behavior. Methods The proposed model uses the network structure to describe valid information in OHCs and treats a recommendation as the task of predicting links between users and threads in the network. Topic nodes are added to the information network to better represent the features of users and threads. The results of the latent Dirichlet allocation (LDA) model describe thread topics and user interests from the perspectives of consumer health vocabulary in OHCs and social support types. The large-scale information network embedding technology LINE is used to mine the node's contextual information from the network structure to obtain the low-dimensional vectors of nodes. We optimize the representation method and similarity calculation of network nodes and enrich the network structure information contained in the recommended features to improve the recommendation effect. Results To verify the proposed model, we collected data from the diabetes forum “Sweet Home.” The experimental results show that the proposed model can effectively extract user interests in threads from the information network and optimize thread recommendation in OHCs.

Integrating Topic Model and Network Embedding for Thread Recommendation

Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general public. 
Design/methodology/approach: A four-step workflow is developed to integrate and disseminate heterogeneous health information from medical associations. First, a raw data repository is developed to manage the original submissions from information providers. Second, each document in the raw data repository is represented in a standardized XML schema. Third, the medical terms are identified and manually annotated, enriching the semantics of health information. Lastly, all the semantically enriched XML documents are converted to HTMLs for online dissemination. 
Findings: A health information website, CHealth, was developed for Chinese speakers. It provides free online access for all without any login or IP constrains. CHealth is available at www.chealth.org.cn . 
Research limitations: The current workflow is time-consuming and labor-intensive due to the lack of information submission/exchange standard and commonly agreed-on consumer health terminology in Chinese. 
Originality/value: In this work, the target audience of the medical library has been extended from traditional academic/professional to the general public. Methodologies in library sciences have been combined with those in consumer health informatics in CHealth development.

Enriching online access to health information for the public: Efforts from a medical library in China

Health professionals and consumers use different terms to express medical events or concerns, which makes the communication barriers between the professionals and consumers. This may lead to bias in the diagnosis or treatment due to the misunderstanding or incomplete understanding. To solve the issue, a consumer health vocabulary was developed to map the consumer-used health terms to professional-used medical terms. In this study, we extracted Chinese consumer health terms from both online health forum and patient education monographs, and manually mapped them to medical terms used by professionals (terms in medical thesauri or in medical books). To ensure the above annotation quality, we developed annotation guidelines. We applied our method to extract consumer-used disease terms in endocrinology, cardiology, gastroenterology and dermatology. In this study, we identified 1349 medical mentions from 8436 questions posted in an online health forum and 1428 articles for patient education monographs. After manual annotation and review, we released 1036 Chinese consumer health terms with mapping to 480 medical terms. Four annotators worked on the manual annotation work following the Chinese consumer health term annotation guidelines. Their average inter-annotator agreement (IAA) score was 93.91% ensuring high consistency of the released terms. We extracted Chinese consumer health terms from online forum and patient education monographs, and mapped them to medical terms used by professionals. Manual annotation efforts have been made for term annotating and mapping. Our study may contribute to the Chinese consumer health vocabulary construction. In addition, our annotated corpus, both the contexts of consumer health terms and consumer-professional term mapping, would be a useful resource for automatic methodology development. The dataset of the Chinese consumer health terms (CHT) is publicly available at http://www.phoc.org.cn/cht/
 .

/pdf/mining-and-standardizing-chinese-consumer-health-terms-4v6f0jandd.pdf

Mining and standardizing chinese consumer health terms.

To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated. In this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process. The resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. We extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.

/pdf/rcorp-a-resource-for-chemical-disease-semantic-extraction-in-3buozn39hx.pdf

RCorp: a resource for chemical disease semantic extraction in Chinese

Rapid advances of technology and development of research in precision medicine domain have led to the production of different types of biomedical data. Standard medical vocabularies were shown to be limited in dealing with such heterogeneous data and consequently, new controlled vocabulary for data integration and normalization has been proposed. In this study, the precision medicine vocabulary (PMV), which is a controlled vocabulary for terms used in precision medicine, is built based on the method of data integration in Unified Medical Language System (UMLS). It now covers ten top semantic types of disease, drug, gene, gene variation and so on. In total of 1,372,967 concepts and 4,567,208 terms have been integrated from widely used databases related with precision medicine.

Building a Controlled Vocabulary for Standardizing Precision Medicine Terms.

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Mining and standardizing chinese consumer health terms.

RCorp: a resource for chemical disease semantic extraction in Chinese

Building a Controlled Vocabulary for Standardizing Precision Medicine Terms.