Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Knowledge Formalization about Perforated Stomach Ulcer on the Basis of Medical Ontology

The article defines knowledge about pelforated stomach ulcer that is formalized on the basis of an ontology model of medical diagnostics domain. The paper describes a base of observations for the disease and also knowledge base which determines a clinical presentation of the disease. The dependences on courses of the disease and process localization are taken into account during knowledge formalizing. The base of knowledge for the disease has the structure that is conventional for contemporary medicine. These knowledge will be used for building a medical intellectual system of for consulting and diagnostics.

Fuzzy-Constrained Graph Pattern Matching in Medical Knowledge Graphs

The research on graph pattern matching(GPM) has attracted a lot of attention. However, most of the research has focused on complex networks, and there are few researches on GPM in the medical field. Hence, with GPM this paper is to make a breast cancer-oriented diagnosis before the surgery. Technically, this paper has firstly made a new definition of GPM, aiming to explore the GPM in the medical field, especially in Medical Knowledge Graphs(MKGs). Then, in the specific matching process, this paper introduces fuzzy calculation, and proposes a multi-threaded bidirectional routing exploration(M-TBRE) algorithm based on depth first search and a two-way routing matching algorithm based on multi-threading. In addition, fuzzy constraints are introduced in the M-TBRE algorithm, which leads to the Fuzzy-M-TBRE algorithm. The experimental results on the two datasets show that compared with existing algorithms, our proposed algorithm is more efficient and effective.

Ten misconceptions regarding decision-making in critical care

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system.Patient complexity,illness severity,and the urgency in initiating proper treatment all contribute to decision-making errors.Clinician-related factors such as fatigue,cognitive overload,and inexperience further interfere with effective decision-making.Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error.This evidence-based review discusses ten common misconceptions regarding critical care decision-making.By understanding how practitioners make clinical decisions and examining how errors occur,strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

Correlation of long-term medication behaviour self-efficacy with social support and medication knowledge of kidney transplant recipients

Aim:This study aimed to explore the correlation of long-term medication behaviour self-efficacy with social support and medication knowledge of kidney transplant recipients.Methods:A convenient sample of kidney transplant recipients in a general hospital in Guangzhou was recruited from November 2016 to January 2017.Self-reported survey data were provided by the kidney transplant recipients using long-term medication behaviour self-efficacy scale(LTMBSES),perceived social support scale(PSSS)and scale for patient-perceived medication knowledge in medication usage.Descriptive statistics and Spearman's correlation analysis were used for data analysis.Results:A total of 195 kidney transplant recipients(132 men and 63 women)were recruited for this study.The mean scores of all LTMBSES dimensions,namely personal attitudes,environmental and taskrelated and behavioural factors,were 32.49±4.34,60.90±7.56 and 32.32±4.40,respectively.The mean scores of three PSSS dimensions,namely,family,friends and significant others,were 24.91±3.42,22.71±4.66 and 22.15±4.26,correspondingly.The mean scores of two five-item knowledge subscale dimensions,namely,general and interaction knowledge,were 9.56±1.07 and 13.02±2.67,respectively.Spearman's correlation analysis showed that social support(rs=0.232,P<0.01)and medication knowledge(rs=0.352,P<0.01)were positively associated with long-term medication self-efficacy in the kidney transplant recipients.Conclusion:The levels of long-term medication self-efficacy,perceived social support and medication knowledge of the kidney transplant recipients were high.Social support and medication knowledge were correlated with self-efficacy.

Tributary Medicine System of the Qing Dynasty: A Preliminary Study

Medicine was one of the articles of tribute paid by other countries and kingdoms to the imperial court of the Qing dynasty(1644–1912).The act of paying tributes and the rituals associated with it enhanced communication and helped establish relationships between ancient China and other nations or territories.The imperial court was generous in return,which attracted many countries to pay tributes.This paper analyzes how medicines as tributes played an important role in consolidating the dominant status of ancient China,and in promoting the exchange of knowledge between Chinese and Western medicine.