Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians\' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.

Precise semantic representation is important for allowing machines to truly comprehend the meaning of natural language text, especially biomedical literature. Although the semantic relations among words in a single sentence may be accurately represented with existing approaches, relations between two sentences cannot yet be accurately modeled, which leads to a lack of contextual information and difficulty in performing interpretable semantic inference. Additionally, it is challenging to merge semantic representations curated by different experts. These critical challenges are insufficiently addressed by existing methods. In this paper, we present a framework for structured semantic representation (FSSR) to address these issues. FSSR uses a double-layer structure Construct that combines Paradigm and Instance to represent the semantics of a word or a sentence. It uses six types of rules to represent the semantic relations between sentence Constructs and uses a Computational Model to represent an action. FSSR is a graph-based representation of semantics, in which a node represents a Construct or a Paradigm. Two nodes are connected by an edge (a rule). In addition, FSSR enables interpretable inference and active acquisition of new information, as illustrated in a case study. This case study models the semantics of a cancer prognostic analysis article and reproduces its text results and charts. We provide a website that visualizes the inference process (http://cragraph.synergylab.cn).

Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients. The knowledge base is formalized as an ontology and decision rules. OncoDoc is another CDSS applied to breast cancer management. The knowledge base is structured as a decision tree. OncoDoc has been routinely used by the multidisciplinary tumor board physicians of the Tenon Hospital (Paris, France) for three years leading to the resolution of 1,861 exploitable decisions. Because we were lacking patient data to assess the DESIREE GL-DSS, we investigated the option of reusing OncoDoc patient data. Taking into account that we have two CDSSs with two formalisms to represent clinical practice guidelines and two knowledge representation models, we had to face semantic and structural interoperability issues. This paper reports how we created 10,681 synthetic patients to solve these issues and make OncoDoc data re-usable by the GL-DSS of DESIREE.

Do children understand the cognitive changes that happen with development? Two experiments examined whether 4- and 6-year-olds understand that, as time passes, children forget some of the things they currently know. In Experiment 1, children were taught the names of a new person and a new object and then were informed that contact with these items will discontinue. Children were asked whether they would know the names tomorrow and as grown-ups. Both age groups demonstrated awareness that forgetting might occur. In Experiment 2, children showed a similar pattern of judgments about a peer\'s knowledge. The findings suggest that knowledge loss is integral to children\'s future thinking and is part of their understanding of the mind as a dynamically changing system.

BACKGROUND: Ontologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies.
OBJECTIVE: The supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases.
METHODS: First, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF.
RESULTS: OntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as \"adolescent female cystic fibrosis patient\") and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis.
CONCLUSIONS: Although cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts.

BACKGROUND: A wide gulf remains between knowledge and clinical practice. Clinical decision support has been demonstrated to be an effective knowledge tool that healthcare organizations can employ to deliver the \"right knowledge to the right people in the right form at the right time\". How to adopt various clinical decision support (CDS) systems efficiently to facilitate evidence-based practice is one challenge faced by knowledge translation research.
METHODS: A computer-aided knowledge translation method that mobilizes evidence-based decision supports is proposed. The foundation of the method is a knowledge representation model that is able to cover, coordinate and synergize various types of medical knowledge to achieve centralized and effective knowledge management. Next, web-based knowledge-authoring and natural language processing based knowledge acquisition tools are designed to accelerate the transformation of the latest clinical evidence into computerized knowledge content. Finally, a batch of fundamental services, such as data acquisition and inference engine, are designed to actuate the acquired knowledge content. These services can be used as building blocks for various evidence-based decision support applications.
RESULTS: Based on the above method, a computer-aided knowledge translation platform was constructed as a CDS infrastructure. Based on this platform, typical CDS applications were developed. A case study of drug use check demonstrates that the CDS intervention delivered by the platform has produced observable behavior changes (89.7% of alerted medical orders were revised by physicians).
CONCLUSIONS: Computer-aided knowledge translation via a CDS infrastructure can be effective in facilitating knowledge translation in clinical settings.

OBJECTIVE: To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information.
METHODS: Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling. We applied this approach to heterogeneous representations of an excerpt of a heart failure summary.
RESULTS: Using a set of finite top-level patterns to derive semantic patterns, we demonstrate that those patterns, or compositions thereof, can be used to represent information from clinical models. Homogeneous querying of the same or similar information, when represented according to heterogeneous clinical models, is feasible.
CONCLUSIONS: Our approach focuses on the meaning embedded in EHRs, regardless of their structure. This complex task requires a clear ontological commitment (ie, agreement to consistently use the shared vocabulary within some context), together with formalization rules. These requirements are supported by semantic patterns. Other potential uses of this approach, such as clinical models validation, require further investigation.
CONCLUSIONS: We show how an ontology-based representation of a clinical summary, guided by semantic patterns, allows homogeneous querying of heterogeneous information structures. Whether there are a finite number of top-level patterns is an open question.

Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods.

BACKGROUND: When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space.
OBJECTIVE: This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes.
METHODS: We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively.
RESULTS: We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction.
CONCLUSIONS: It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency.