Fuzzy ontology-based Medical Information Retrieval

In recent years, information retrieval technology is widely used in the medical industry. How to effectively retrieve electronic medical record has become a hot issue in the field of information retrieval. Medical terms occupy an important position in the electronic medical record (EMR) retrieval, and they are usually used to limit the retrieval conditions, so they suggest the user’s search intention. Aiming at the importance of medical terms, a method of query reformulation based on medical term reweighting is proposed to improve the performance of EMR retrieval. Firstly, the method filtrates medical terms from the origin query. Then, each medical term is weighted by its own self-information which can be estimated from the document set. Finally, the new query is constructed by combining the weighted medical terms and origin query proportionally. Experimental results on the TREC dataset show that our method performed above the baseline in three performance metrics: MAP (+ 14.2%), bpref (+ 8.0%), and P@10 (+ 9.6%).

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

Ontologies have proven their utility in the area of Information Retrieval. However, building and updating ontologies manually is a long and tedious task. Moreover, crisp ontologies are not capable to support uncertain information. One interesting solution is to integrate fuzzy logic into ontology to handle vague and imprecise information. This paper presents a method for individual fuzzy ontology building. The key aspects in our proposal are: (1) an automatic building of an individual fuzzy ontology; (2) a query reformulation based, on the one hand, on the weights associated with the concepts and all existing relations in the fuzzy ontology and, on the other hand, on users' preferences, (3) an update of the membership concepts and relations' values after each users search, and (4) the use of the proposed fuzzy ontology and service ontology to individually classify documents by services. Our method has endured a twofold evaluation. Firstly, we have evaluated the impact of the update and the weights' variations on the search results. Secondly, we have studied how the query reformulation has led to a quality results improvement, both in terms of precision and recall.

Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.

Over the past decade, information retrieval has emerged as an active research area in the application of fuzzy set theory. Fuzzy information retrieval utilizes fuzzy sets to represent documents, membership degrees for query term relevance, fuzzy logical operators to define queries, and fuzzy compatibility measures to assess the retrieval status value of a document. This paper presents an overview of fuzzy relational databases and fuzzy information retrieval. A general description of the main components of fuzzy information retrieval are given: document representation, query representation, computer-aided query formulation, document retrieval status, and performance measures. Examples of areas currently being researched are provided. The relation between fuzzy information retrieval and fuzzy relational databases is examined.

Survey on Information Retrieval Accuracy in Biomedical Systems

In this paper, we describe the methodology and experimental analysis of a twofold strategy for the retrieval of medical relevant information: a ranking fusion and a query reformulation approach. In particular, the query reformulation approach is based on the idea that a query is composed of two parts: the primary term and the secondary term of the query, and that these two parts can be substituted with alternative terms to create a reformulation of the original query. The goal of our work is to evaluate the performances of a search engine over 1) manual query variants; 2) different retrieval functions; 3) w/out pseudo-relevance feedback; 4) reciprocal ranking fusion. We describe the experiments based on the CLEF eHealth 2021 Consumer Health Search Task dataset. The results show that 1) a ranking fusion approach of the baseline models improves MAP significantly; 2) manual query variants open new questions about possible an unintentional bias in the pool of documents that were selected for relevance assessment.

Under-specified queries often lead to undesirable search results that do not contain the information needed. This problem gets worse when it comes to medical information, a natural human demand everywhere. Existing search engines on the Web often are unable to handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users’ original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the qualified tag neighbors to expand an entry query. The proposed approach is evaluated by using MedWorm medical article collection and results show considerable precision improvements over state-of-the-art approaches.

It is reported that digital dashboard systems in hospitals provide a user interface (UI) that can centrally manage and retrieve various information related to patients in a single screen, support the decision-making of medical professionals on a real time basis by integrating the scattered medical information systems and core work flows, enhance the competence and decision-making ability of medical professionals, and reduce the probability of misdiagnosis. However, the digital dashboard systems of hospitals reported to date have some limitations when medical professionals use them to generally treat inpatients, because those were limitedly used for the work process of certain departments or developed to improve specific disease-related indicators. Seoul National University Bundang Hospital developed a new concept of EMR system to overcome such limitations. The system allows medical professionals to easily access all information on inpatients and effectively retrieve important information from any part of the hospital by displaying inpatient information in the form of digital dashboard. In this study, we would like to introduce the structure, development methodology and the usage of our new concept.

Introduction Health literacy plays a crucial role in enabling patients to understand and effectively use medical information. As technology rapidly advances, the significance of health literacy becomes even more pronounced, particularly in comprehending complex medical information. Artificial Intelligence (AI) platforms have garnered significant attention for their remarkable ability to generate automated responses to a wide range of prompts. However, their capacity to assess the quality and readability of provided text remains uncertain. Given the growing prominence of AI web assistant tools, we hypothesized that integrating these tools into patients' web searches could enhance the retrieval of accurate medical information. Objective To evaluate the proficiency of Conversational Generative Pre-Trained Transformer (ChatGPT) in assessing readability, and utilizing the DISCERN tool to assess quality of online content regarding shock wave therapy for erectile dysfunction. Methods Websites were generated using a Google search of “shock wave therapy for erectile dysfunction” with location filters disabled. Readability was analyzed using the Readable software (Readable.com, Horsham, United Kingdom). Quality was assessed independently by three reviewers using the DISCERN tool. The same plain text files collected were inputted into ChatGPT to determine whether it produced comparable metrics for readability and quality. Results The study results revealed a notable disparity between ChatGPT's readability assessment and that obtained from a reliable tool, Readable.com (p<0.05). This indicates a lack of alignment between ChatGPT's algorithm and that of established tools, such as Readable.com. Similarly, the DISCERN score generated by ChatGPT differed significantly from the scores generated manually by human evaluators (p<0.05), suggesting that ChatGPT may not be capable of accurately identifying poor-quality information sources regarding shock wave therapy as a treatment for erectile dysfunction. Conclusions ChatGPT’s evaluation of the quality and readability of online text regarding shockwave therapy for erectile dysfunction differs from that of human raters and trusted tools. ChatGPT's current capabilities were not sufficient for reliably assessing the quality and readability of textual content. Further research is needed to elucidate the role of AI in the objective evaluation of online medical content in other fields. Continued development in AI and incorporation of tools such as DISCERN into AI software may enhance the way patients navigate the web in search of high-quality medical content in the future. Disclosure No.

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy ontologies have been extensively investigated and increasingly created. Therefore, it is critical to develop scalable and efficient fuzzy ontology storage mechanism. The fuzzy relational database may be a good candidate for storing fuzzy ontologies because of the widespread use and mature techniques. In this paper, we propose an approach for storing fuzzy ontologies in fuzzy relational databases. The elements of fuzzy ontologies are introduced first, where most of constructors of fuzzy ontologies are considered. On this basis, we propose an approach for storing all of these elements of fuzzy ontologies in fuzzy relational databases, and an example is provided throughout the paper to well explain the approach.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.

A new approach for fuzzy information retrieval based on weighted power-mean averaging operators

Construction of fuzzy ontologies from fuzzy XML models