scholarly journals Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study in Pituitary Adenoma (Preprint)

10.2196/28218 ◽  
2021 ◽  
Author(s):  
An Fang ◽  
Pei Lou ◽  
Jiahui Hu ◽  
Wanqing Zhao ◽  
Ming Feng ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Medical Knowledge ◽  
Knowledge Graph ◽  
Fusion Model

scholarly journals KGPA: Construction of Knowledge Graph for Pituitary Adenoma (Preprint)

2021 ◽  
Author(s):  
An Fang ◽  
Pei Lou ◽  
Jiahui Hu ◽  
Wanqing Zhao ◽  
Ming Feng ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Data Fusion ◽  
Medical Knowledge ◽  
Medical Data ◽  
Knowledge Graph ◽  
Fusion Method ◽  
Fusion Model ◽  
Construction Of Knowledge ◽  
Icd 10 ◽  
Disease Entities

BACKGROUND Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma are still very difficult. Misdiagnosis and recurrence occur from time to time, and experienced neurosurgeons are in serious shortage. Knowledge graphs can help interns quickly understand the medical knowledge related to pituitary tumor. OBJECTIVE The aim of this paper is to integrate the data of pituitary adenomas from reliable sources and construct a knowledge graph, and use the knowledge graph for knowledge discovery. METHODS A method of constructing a knowledge graph of diseases was introduced and used to build a knowledge graph for pituitary adenoma (KGPA). The schema of the KGPA was manually constructed. Information of pituitary adenoma were automatically extracted from EMR and the medical websites through the CRF model and web wrappers we designed. An entity fusion method was proposed, based on the head and tail entity fusion models, to fuse the data from heterogeneous sources. The disease entities were standardized to ICD-10. RESULTS Data was extracted from 300 EMRs of pituitary adenoma and 4 medical portals. Entity fusion was carried out by using the data fusion model we proposed. The accuracy of the head and tail entity fusion were more than 97%. Part of the triples were selected for evaluation, and the accuracy was 95.4%. CONCLUSIONS This paper introduced an approach to construct KGPA and proposed a data fusion method suitable for medical data. The evaluation results show that the data in KGPA is of high quality. The constructed KGPA can help physicians in their clinical practice.

Knowledge Recommendation System for Human-Robot Collaborative Disassembly Using Knowledge Graph

2021 ◽  
Author(s):  
Yang Hu ◽  
Yiwen Ding ◽  
Feng Xu ◽  
Jiayi Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Recommendation System ◽  
Industrial Robots ◽  
Knowledge Graph ◽  
Disassembly Process ◽  
Knowledge Based ◽  
Manufacturing Efficiency ◽  
Human Operators ◽  
Traditional Manufacturing ◽  
Human Robot Collaboration

Abstract In recent years, more and more attention has been paid to Human-Robot Collaborative Disassembly (HRCD) in the field of industrial remanufacturing. Compared with the traditional manufacturing, HRCD helps to improve the manufacturing flexibility with considering the manufacturing efficiency. In HRCD, knowledge could be obtained from the disassembly process and then provides useful information for the operator and robots to execute their disassembly tasks. Afterwards, a crucial point is to establish a knowledge-based system to facilitate the interaction between human operators and industrial robots. In this context, a knowledge recommendation system based on knowledge graph is proposed to effectively support Human-Robot Collaboration (HRC) in disassembly. A disassembly knowledge graph is constructed to organize and manage the knowledge in the process of HRCD. After that, based on this, a knowledge recommendation procedure is proposed to recommend disassembly knowledge for the operator. Finally, the case study demonstrates that the developed system can effectively acquire, manage and visualize the related knowledge of HRCD, and then assist the human operator to complete the disassembly task by knowledge recommendation, thus improving the efficiency of collaborative disassembly. This system could be used in the human-robot collaboration disassembly process for the operators to provide convenient knowledge recommendation service.

scholarly journals Knowledge, Attitude, and Practice of Physicians Regarding Vaccinations in Yerevan, Armenia: A Case Study of HPV

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1188
Author(s):  
Arman R. Badalyan ◽  
Marine Hovhannisyan ◽  
Gayane Ghavalyan ◽  
Mary M. Ter-Stepanyan ◽  
Rory Cave ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Vaccine Coverage ◽  
Medical Knowledge ◽  
Hpv Infection ◽  
Healthcare Sector ◽  
Vaccine Hesitancy ◽  
Primary Data ◽  
Cross Sectional ◽  
New Vaccines

This paper highlights the low levels of vaccine coverage and high levels of reported vaccination hesitancy in Yerevan, Armenia, that present profound challenges to the control of disease through routine vaccination programmes. We draw on investigations of hesitancy towards the introduction of new vaccines, using the Human Papillomavirus (HPV) vaccine Gardasil as a case study, to interrogate underlying challenges to vaccine acceptance. We analyse primary data from the introduction of Gardasil, first used in Armenia in 2017, to investigate how levels of medical knowledge amongst physicians in 20 health facilities in Yerevan, Armenia, regarding vaccine science influence attitudes towards the introduction of a newly developed vaccine. A questionnaire-based cross-sectional study was completed by 348 physicians between December 2017 and September 2018. The responding physicians displayed a respectable level of knowledge and awareness regarding vaccination with respect to some characteristics (e.g., more than 81% knew that HPV infection was commonly asymptomatic, 73% knew that HPV infection was implicated in most cervical cancers, and 87% knew that cervical cancer is the most prevalent cancer amongst women) but low knowledge and poor understanding of other key issues such as the age at which women were most likely to develop cervical cancer (only 15% answered correctly), whether or not the vaccine should be administered to people who had already been infected (27% answered correctly) and whether sexually active young people should be treated for infection before vaccination (26% answered correctly). The study suggests that the drivers of vaccine hesitancy are complex and may not be consistent from vaccine to vaccine. The Armenian healthcare sector may need to provide additional training, awareness-raising and educational activities alongside the introduction of new vaccines to improve understanding of and trust in vaccination programmes.

scholarly journals BioKEEN: a library for learning and evaluating biological knowledge graph embeddings

2019 ◽  
Vol 35 (18) ◽  
pp. 3538-3540 ◽  
Author(s):  
Mehdi Ali ◽  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Jens Lehmann ◽  
Hajira Jabeen
Keyword(s):  
Supplementary Information ◽  
Knowledge Graph ◽  
Biological Knowledge ◽  
Command Line ◽  
Graph Embeddings ◽  
Command Line Interface ◽  
Software Ecosystem ◽  
Mapping Resource ◽  
Significant Attention

Abstract Summary Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. Availability and implementation BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

2019 ◽  
Vol 33 ◽  
pp. 7346-7353
Author(s):  
Lin Xu ◽  
Qixian Zhou ◽  
Ke Gong ◽  
Xiaodan Liang ◽  
Jianheng Tang ◽  
...  
Keyword(s):  
Natural Language ◽  
Medical Diagnosis ◽  
Expert Knowledge ◽  
Medical Knowledge ◽  
Knowledge Graph ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Self Reports ◽  
Medical Dialogue ◽  
End To End

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on datadriven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptomdisease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats stateof-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.

Language localization in scientific drug information

2016 ◽  
Vol 3 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Maria-Cornelia Wermuth
Keyword(s):  
Cultural Adaptation ◽  
Drug Information ◽  
Medical Knowledge ◽  
Drug Manufacturer ◽  
Medical Discourse ◽  
European Medicines Agency ◽  
Source Text ◽  
Linguistic Features ◽  
Written Materials

Although localization is, in the first place, related to the cultural adaptation and translation of software and websites, it is important for written materials as well. In this paper we investigate how specialized medical discourse used in the Summary of Product Characteristics (SmPC) is localized in patient leaflets (PL). Both documents are issued by the European Medicines Agency (EMA) and provide detailed information on the product compiled and distributed by the drug manufacturer, after EMA review and approval. We describe by means of a case study the formal and linguistic features of SmPCs and PLs and we investigate how the specialized source text is localized in its patient-friendly version. The aim of this investigation is to increase awareness and understanding of localization strategies adopted on the intralingual level in the communication of scientific-medical knowledge to a non-expert audience.

scholarly journals SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation

2021 ◽  
Vol 23 ◽  
pp. 100174
Author(s):  
Fan Gong ◽  
Meng Wang ◽  
Haofen Wang ◽  
Sen Wang ◽  
Mengyue Liu
Keyword(s):  
Medical Knowledge ◽  
Graph Embedding ◽  
Knowledge Graph
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