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

Influence of the Reflex Math Fact Fluency Program on Math Scores by Tammy D. Cress MA, Winona State University, 2003 BS, Southeastern University, 1985 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Education

/pdf/influence-of-the-reflex-math-fact-fluency-program-on-math-2egxojbt3m.pdf

Influence of the Reflex Math Fact Fluency Program on Math Scores

Hazard and Operability Study (HAZOP) is a Hazard analysis method based on system engineering, which analyzes the potential risks that may exist in the process of technological operation through a certain joint, and provides guidance for subsequent control and management However, for a long time, the form of HAZOP analysis results is difficult to unify In order to solving this problem, information needs to be extracted to establish a database, and Named Entity Recognition (NER) is a key step in this process In this paper, a CNN-Bilstm-CRF neural network model is proposed for entity recognition of HAZOP text A novel activation function is used in the convolutional neural network (CNN) for parameter optimization, and different weight initialization methods are selected to optimize the results The corpus used in this paper is the HAZOP analysis report of the oil synthesis equipment of the self-marked coal seam indirect liquefaction project Verified on the Tensorflow, the experimental results show that this method has a good recognition effect on HAZOP text entities, and the F1 value reaches 9111%

HAZOP Text Named Entity Recognition using CNN-BilSTM-CRF Model

With the wide application of information technique in the medical field, various medical information systems have produced massive medical data. The problem of mining knowledge from medical data has been extensively studied. For the medical named entity recognition problem, Bidirectional Long-Short-Term Memory model with Conditional Random Field (biLSTM-CRF) is commonly used. However, CRF cannot capture long term output label relations because of the Markov assumptions. To solve this problem, we propose to use a bidirectional Long-Short-Term Memory model with Label Attention Network (biLSTM-LAN) for medical named entity recognition. The experimental results show that using LAN to replace CRF can improve the recognition accuracy when their number of parameters are similar.

BiLSTM-LAN based Medical Named Entity Recognition

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).

Named entity recognition in electronic medical records is of great significance to the construction of medical knowledge maps. This paper proposes a model of bidirectional Long Short-Term Memory with a conditional random field layer(BiLSTM-CRF). In terms of simultaneously identifying 5 types of clinical entities from CCKS2018 Chinese EHRs corpus, the BiLSTM-CRF model finally achieved better performance than the baseline CRF model (F-score of 84.23% vs 82.49%).

Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer.

To identify knowledge of combination therapy for chronic diseases, high-quality manually curated corpora are required. In this study, we describe the construction work of a corpus for chemical disease semantic extraction in Chinese (RCorp) from a collection of Chinese biomedical abstracts. During the RCorp corpus construction, we used domain-existing guidelines as references, and incorporated annotation tools to the standard annotation process. The resulting corpus consists of 339 Chinese biomedical articles with 2,367 annotated chemicals, 2,113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. RCorp 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. The result analysis of the corpus proves the quality of the corpus for the combination therapy of chronic diseases knowledge discovery task.

RCorp: a Resource for Chemical Disease Semantic Extration in Chinese

Different groups of audiences in health care: health professionals and health consumers, each have different information needs. Health monographs targeting different audiences are created by leveraging readers' background knowledge. The NCI's Physician Data Query (PDQ®) Cancer Information Summaries provide parallel cancer information and education resources with different target audiences. In this paper, we used targeted audience-specific cancer information PDQs to measure characteristic differences on the element level between audiences. In addition, we compared vocabulary coverage. Results show a significant difference between the professional and patient version of cancer monographs in both content organization and vocabulary. This study provides a new view to assess targeted audience-specific health information, and helps editors to improve the quality and readability of health information.

Characterizing Health Information for Different Target Audiences.

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Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer.

RCorp: a Resource for Chemical Disease Semantic Extration in Chinese

Characterizing Health Information for Different Target Audiences.