Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

AbstractObjectiveTo explore multiple relationships in traditional Chinese medicine (TCM) knowledge by comparing binary and multiple relationships during knowledge organization.MethodsCharacteristics of binary and multiple semantic relationships as well as their associations are described. A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces. The classification includes parallel multiple relationships, restricted multiple relationships, multiple relationships that involve time, and multiple relationships that involve time restriction. Next, construction of multiple semantic relationships for TCM concepts in each classification using Protégé, an ontology editing tool is described.ResultsProtégé is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.ConclusionWhen applied in TCM, the semantic relationships constructed by Protégé are superior than those constructed by correlation and/or attribute relationships, but less ideal than those constructed by the human cognitive process

Consumer experience and satisfaction of private health management service enterprises in China

The private health management service industry in China still has a long way to enjoy the same maturity and success as the Chinese public healthcare enterprises. Carrying out research about private health management service company in China has both theoretical and practical values. Through the methods of literature research, this thesis studies on the development, characteristics and situation of private health management service industry in China, as well as relevant literature review on health management. In China, most medical institutions provide related health management services, among which the physical examination is the most important item. Both strengths and weaknesses of public and private health management service enterprises are summarized through SWOT analysis. The research method of content analysis is brought into the study of consumer experience and satisfaction about physical examination services in China. Taking Meinian Onehealth Healthcare Holdings Co., Ltd. as an example, the main factors define customer experience and determine their satisfaction and dissatisfaction is researched through content analysis of online reviews from websites by ROST-CM software, and both the positive and negative reviews are analyzed respectively from the aspects of word frequency and semantic network. The main factors influencing customer satisfaction are divided into six themes: service, technology, environment, price, procedure and consultation. In service, the service attitude of doctors and staff is the most important factor for customers. In the theme of technology, the advanced level of examination equipment and the professional level of medical staff, as perceived performance, are more important factors affecting the consumer satisfaction. In the theme of procedure, optimizing physical examination procedure and shortening waiting time are important factors to improve customer satisfaction. In the theme of consultation, strengthening health education and keeping track of customers' health situation are also important factors to improve satisfaction.Based on the results of analysis, five effective marketing strategies in the health management industry is put forward, mainly including service quality strategy, brand strategy, competing strategy, customer relationship strategy and price strategy.A fim de alcançar a maturidade e sucesso da saúde pública, ainda há um longo caminho a percorrer pela indústria de serviços de gestão de saúde privados na China. Por isso, existe valor teórico e prático para a realização de investigação em empresas privadas de serviços de gestão de saúde na China. Este trabalho introduz sistematicamente o desenvolvimento, as características e o estado actual da indústria de serviços de gestão de saúde da China por meio de pesquisas bibliográficas e darevisão de literatura sobre gestão de saúde. O trabalho compara as forças e fraquezas das organizações de serviços de gestão de saúde privadas e públicas. Neste trabalho, o método de pesquisa de análise de conteúdo é introduzido no estudo da experiência e satisfação do consumidor com os serviços privados de gestão de saúde na China. Como caso de estudo foi usado o Meinian Onehealth Healthcare Holdings Co., Ltd. Com recurso ao software ROST-CM, foi efetuada a análise de conteúdo dos comentários online, e identificados os principais factores que influenciaram a experiência do cliente. A análise de elogios e críticas negativas foi realizada em termos de frequência de palavras e rede semântica. Os principais factores que afectam a satisfação de cliente são divididos em seis temas: serviço, tecnologia, ambiente, preço, processo e literacia. Ao servir clientes, a atitude de médicos e funcionários é particularmente importante para a satisfação de clientes. Em termos de tecnologia, o equipamento de exame médico avançado e o nível profissional de equipa médica são benefícios perceptíveis do exame médico e factores importantes de satisfação de consumidor. O ambiente do centro de exames médicos não causa tanto impacto como o serviço e a tecnologia. Em termos de preço, consumidores costumam usar "acessível" ou "caro" para expressar sua avaliação subjectiva do número de itens e o valor de serviços recebidos em um exame médico. Em termos de processo, optimizar o processo de exame médico e reduzir o tempo das filas de espera são factores importantes para a satisfação deexame médico. Em termos de literacia, fortalecer a educação em saúde e acompanhar a saúde de clientes também são factores importantes para a satisfação com o exame médico. Tendo por base os resultados obtidos, são propostas cinco estratégias de marketing eficazes adaptadas às empresas de serviços de gestão de saúde, designadamente estratégias de promoção de serviços, estratégias de promoção de marca, estratégias de concorrência, estratégias de relacionamento com clientes e estratégias de preço

Upper Tag Ontology (UTO) For Integrating Social Tagging Data

Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube)

Data Science and Ebola

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit

Clinical Decision Support System for Unani Medicine Practitioners

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of Artificial Intelligence in Clinical Decision Support System for Unani Medicine

Adverse drug reaction extraction on electronic health records written in Spanish

148 p.This work focuses on the automatic extraction of Adverse Drug Reactions (ADRs) in Electronic HealthRecords (EHRs). That is, extracting a response to a medicine which is noxious and unintended and whichoccurs at doses normally used. From Natural Language Processing (NLP) perspective, this wasapproached as a relation extraction task in which the drug is the causative agent of a disease, sign orsymptom, that is, the adverse reaction.ADR extraction from EHRs involves major challenges. First, ADRs are rare events. That is, relationsbetween drugs and diseases found in an EHR are seldom ADRs (are often unrelated or, instead, related astreatment). This implies the inference from samples with skewed class distribution. Second, EHRs arewritten by experts often under time pressure, employing both rich medical jargon together with colloquialexpressions (not always grammatical) and it is not infrequent to find misspells and both standard andnon-standard abbreviations. All this leads to a high lexical variability.We explored several ADR detection algorithms and representations to characterize the ADR candidates.In addition, we have assessed the tolerance of the ADR detection model to external noise such as theincorrect detection of implied medical entities implied in the ADR extraction, i.e. drugs and diseases. Westtled the first steps on ADR extraction in Spanish using a corpus of real EHRs

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe