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\"Based on current medical research, Native American and naturopathic doctor Nicole Redvers identifies traditional healing methods developed centuries ago that address modern ailments and medical processes\"-- Provided by publisher.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.

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.

Purpose The study aims to assess ChatGPT performance on the Residency Admission National Exam to evaluate ChatGPT’s level of medical knowledge compared to graduate medical doctors in Italy. Methods ChatGPT3 was used in June 2023 to undertake the 2022 Italian Residency Admission National Exam—a 140 multiple choice questions computer-based exam taken by all Italian medical graduates yearly, used to assess basic science and applied medical knowledge. The exam was scored using the same criteria defined by the national educational governing body. The performance of ChatGPT was compared to the performance of the 15,869 medical graduates who took the exam in July 2022. Lastly, the integrity and quality of ChatGPT’s responses were evaluated. Results ChatGPT answered correctly 122 out of 140 questions. The score ranked in the top 98.8 th percentile among 15,869 medical graduates. Among the 18 incorrect answers, 10 were evaluating direct questions on basic science medical knowledge, while 8 were evaluating candidates’ applied clinical knowledge and reasoning under the form of case presentation. Errors were logical (2 incorrect answers) and informational in nature (16 incorrect answers). Explanations to the correct answers were all evaluated as “appropriate.” Comparison to national statistics related to the minimal score needed to match into each specialty, demonstrated that the performance of ChatGPT would have granted the candidate a match into any specialty. Conclusion ChatGPT proved to be proficient in basic science medical knowledge and applied clinical knowledge. Future research should assess the impact and reliability of ChatGPT in clinical practice.

Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

The social construction of illness is a major research perspective in medical sociology. This article traces the roots of this perspective and presents three overarching constructionist findings. First, some illnesses are particularly embedded with cultural meaning—which is not directly derived from the nature of the condition—that shapes how society responds to those afflicted and influences the experience of that illness. Second, all illnesses are socially constructed at the experiential level, based on how individuals come to understand and live with their illness. Third, medical knowledge about illness and disease is not necessarily given by nature but is constructed and developed by claims-makers and interested parties. We address central policy implications of each of these findings and discuss fruitful directions for policy-relevant research in a social constructionist tradition. Social constructionism provides an important counterpoint to medicine's largely deterministic approaches to disease and illness, and it can help us broaden policy deliberations and decisions.

This paper investigates a web-based, medical research network that relies on patient self-reporting to collect and analyze data on the health status of patients, mostly suffering from severe conditions. The network organizes patient participation in ways that break with the strong expert culture of medical research. Patient data entry is largely unsupervised. It relies on a data architecture that encodes medical knowledge and medical categories, yet remains open to capturing details of patient life that have as a rule remained outside the purview of medical research. The network thus casts the pursuit of medical knowledge in a web-based context, marked by the pivotal importance of patient experience captured in the form of patient data. The originality of the network owes much to the innovative amalgamation of networking and computational functionalities built into a potent social media platform. The arrangements the network epitomizes could be seen as a harbinger of new models of organizing medical knowledge creation and medical work in the digital age, and a complement or alternative to established models of medical research.

Background Sharing knowledge among scientists during global health emergencies is a critical issue. So, this study investigates knowledge-sharing behavior and attitude among staff members of 19 Medical schools in Egyptian universities during the COVID-19 pandemic. Methods Across-sectional study was conducted using a web-based questionnaire. A total of 386 replies from the 10,318 distributed questionnaires were analyzed. Descriptive statistics were computed using SPSS (version 22) to summarize the demographic data. Inferential statistics such as the independent and chi-square test were used to achieve the study aims. Results More than half of the respondents (54.4%) indicated that their levels of knowledge of COVID-19 were good. Most participants (72.5%) reported that scientific publications and international websites were the most reliable source of their knowledge concerning COVID-19. More than 46% stated they sometimes share their knowledge. The lack of time to share and organizational culture were the most important factors that could affect their knowledge sharing. Additionally, about 75% of participants shared knowledge about treatment.