Multimodal medical case retrieval using the Dezert-Smarandache theory.

International audienceMost medical images are now digitized and stored with semantic information, leading to medical case databases. They may be used for aid to diagnosis, by retrieving similar cases to those in examination. But the information are often incomplete, uncertain and sometimes conflicting, so difficult to use. In this paper, we present a Case Based Reasoning (CBR) system for medical case retrieval, derived from the Dezert-Smarandache theory, which is well suited to handle those problems. We introduce a case retrieval specific frame of discernment theta, which associates each element of theta with a case in the database; we take advantage of the flexibility offered by the DSmT's hybrid models to finely model the database. The system is designed so that heterogeneous sources of information can be integrated in the system: in particular images, indexed by their digital content, and symbolic information. The method is evaluated on two classified databases: one for diabetic retinopathy follow-up (DRD) and one for screening mammography (DDSM). On these databases, results are promising: the retrieval precision at five reaches 81.8% on DRD and 84.8% on DDSM

Computational Dynamic Features Extraction from Anonymized Medical Images

Images depict clearer meaning than written words and this is reason they are used in a variety of human endeavors, including but not limited to medicine. Medical image datasets are used in medical environment to diagnose and confirm medical disorders for which physical examination may not be sufficient. However, the medical profession's ethics of patient confidentiality policy creates barrier to availability of medical datasets for research; thus, this research work was able to solve the above stated barrier through anonymization of sensitive identity information. Furthermore, the Content Based Image Retrieval (CBIR) using texture as the content was developed to overcome the challenge of information overloading associated with data retrieval systems. Images acquired from various imaging modalities and placed into Digital Imaging and Communications in Medicine (DICOM) formats were obtained from several hospitals in Nigeria. The database of these images was created and consequently anonymized, then a new anonymized database was created. On anonymized images, feature extraction was done using textures as content and the content was considered for the implementation of retrieval system. The anonymized images were tested in DICOM view to see if all files were successfully anonymized; the result obtained was 100%. A texture retrieval test was performed, and the number of precisely returned search images using the Similarity Distance Measure formulae resulted in a significant reduction in image overload. Thus, this research work solved the problem of non-availability of datasets for researchers in medical imaging field by providing datasets that can be used without violating law of patient confidentiality by the interested parties. It also solves the problem of hackers obtaining useful information about patients’ datasets. The CBIR using texture as content also enhances and solves the problem of information overloading

Reading databases: slow information interactions beyond the retrieval paradigm

Purpose In this conceptual essay, the purpose of this paper is to argue that the structure of databases and other information systems provides valuable information beyond their content. The author contends that reading databases – as a separate, distinct activity from retrieving and reading the documents that databases contain – is an under-studied form of human-information interaction. Because the act of reading databases encourages awareness, reflection, and control over information systems, the author aligns the author’s proposal with “slow” principles, as exemplified by the slow food movement. Design/methodology/approach This paper presents an extended argument to demonstrate the value of reading a database. Reading a database involves understanding the relationship between database structure and database content as an interpretation of the world. For example, when a supermarket puts vermicelli in the pasta section but rice vermicelli in the Asian section, the supermarket suggests that rice vermicelli is more “Asian” than “noodle.” To construct the author’s argument, the author uses examples that range from everyday, mundane activities with information systems (such as using maps and automated navigation systems) to scientific and technical work (systematic reviews of medical evidence). Findings The slow, interpretively focused information interactions of reading databases complement the “fast information” approach of outcome-oriented retrieval. To facilitate database reading activities, research should develop tools that focus user attention on the application of database structure to database contents. Another way of saying this is that research should exploit the interactive possibilities of metadata, either human-created or algorithmically generated. Originality/value This paper argues that information studies research focuses too heavily on seeking and retrieval. Seeking and retrieval are just two of the many interactions that constitute our everyday activities with information. Reading databases is an area particularly ripe with design possibilities. </jats:sec

Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD's IMF.

In this paper, we address the problem of medical ddiagnosis aid through content based image retrieval methods. We propose to characterize images without extracting local features, by using global information extracted from the image Bidimensional Empirical Mode Decomposition (BEMD). This method decompose image into a set of functions named Intrinsic Mode Functions (IMF) and a residu. The generalized Gaussian density function (GGD) is used to represent the coefficients derived from each IMF, and the Kullback–Leibler Distance (KLD) compute the similarity between GGDs. Retrieval efficiency is given for different databases including a diabetic retinopathy, and a face database. Results are promising: the retrieval efficiency is higher than 85% for some cases

Multilingual query expansion in the Svemed+ bibliographic database : a case study

SveMed+ is a bibliographic database covering Scandinavian medical journals. It is produced by the University Library of Karolinska Institutet in Sweden. The bibliographic references are indexed with terms from the Medical Subject Headings (MeSH) thesaurus. The MeSH has been translated into several languages including Swedish, making it suitable as the basis for multilingual tools in the medical field. The data structure of SveMed+ closely mimics that of PubMed/MEDLINE. Users of PubMed/MEDLINE and similar databases typically expect retrieval features that are not readily available off-the-shelf. The SveMed+ interface is based on a free text search engine (Solr) and a relational database management system (Microsoft SQL Server) containing the bibliographic database and a multilingual thesaurus database. The thesaurus database contains medical terms in three different languages and information about relationships between the terms. A combined approach involving the Solr free text index, the bibliographic database and the thesaurus database allowed the implementation of functionality such as automatic multilingual query expansion, faceting and hierarchical explode searches. The present paper describes how this was done in practice.NoneAccepte

An Associative Semantic Network for Machine-Aided Indexing, Classification and Searching

Capturing and exploiting textual database associations has played a pivotal role in the evolution of automated information systems. A variety of statistical, linguistic and artificial intelligence approaches have been described in the literature.Many of these R and D concepts and techniques are now being incorporated into commercially available search systems and services. This paper discusses prior work and reports on research in progress aimed at creating and utilizing a global semantic associative database, AURA (Associative User Retrieval Aid) to facilitate machine-assisted indexing, classification and searching in the large-scale information processing environment of NLM's core bibliographic databases, MEDLINE and CATLINE. AURA is a semantic network of over two million natural language phrases derived from more than a million MEDLINE titles. These natural language phrases are associatively linked to NLM's MeSH (Medical Subject Headings) and UMLS Metathesaurus (Unified Medical Language System) controlled vocabulary and classification resources

Emerging multidisciplinary research across database management systems

The database community is exploring more and more multidisciplinary avenues: Data semantics overlaps with ontology management; reasoning tasks venture into the domain of artificial intelligence; and data stream management and information retrieval shake hands, e.g., when processing Web click-streams. These new research avenues become evident, for example, in the topics that doctoral students choose for their dissertations. This paper surveys the emerging multidisciplinary research by doctoral students in database systems and related areas. It is based on the PIKM 2010, which is the 3rd Ph.D. workshop at the International Conference on Information and Knowledge Management (CIKM). The topics addressed include ontology development, data streams, natural language processing, medical databases, green energy, cloud computing, and exploratory search. In addition to core ideas from the workshop, we list some open research questions in these multidisciplinary areas

ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge

Recent large language models (LLMs) in the general domain, such as ChatGPT, have shown remarkable success in following instructions and producing human-like responses. However, such language models have yet to be adapted for the medical domain, resulting in poor accuracy of responses and an inability to provide sound advice on medical diagnoses, medications, etc. To address this problem, we fine-tuned our ChatDoctor model based on 100k real-world patient-physician conversations from an online medical consultation site. Besides, we add autonomous knowledge retrieval capabilities to our ChatDoctor, for example, Wikipedia or a disease database as a knowledge brain. By fine-tuning the LLMs using these 100k patient-physician conversations, our model showed significant improvements in understanding patients' needs and providing informed advice. The autonomous ChatDoctor model based on Wikipedia and Database Brain can access real-time and authoritative information and answer patient questions based on this information, significantly improving the accuracy of the model's responses, which shows extraordinary potential for the medical field with a low tolerance for error. To facilitate the further development of dialogue models in the medical field, we make available all source code, datasets, and model weights available at: https://github.com/Kent0n-Li/ChatDoctor

Emerging multidisciplinary research across database management systems

The database community is exploring more and more multidisciplinary avenues: Data semantics overlaps with ontology management; reasoning tasks venture into the domain of artificial intelligence; and data stream management and information retrieval shake hands, e.g., when processing Web click-streams. These new research avenues become evident, for example, in the topics that doctoral students choose for their dissertations. This paper surveys the emerging multidisciplinary research by doctoral students in database systems and related areas. It is based on the PIKM 2010, which is the 3rd Ph.D. workshop at the International Conference on Information and Knowledge Management (CIKM). The topics addressed include ontology development, data streams, natural language processing, medical databases, green energy, cloud computing, and exploratory search. In addition to core ideas from the workshop, we list some open research questions in these multidisciplinary areas

Integrated Access to a Large Medical Literature Database

Project INCARD (INtegrated CARdiology Database) has adapted the CODER (COmposite Document Expert/effective/extended Retrieval) system and LEND (Large External Network object oriented Database) to provide integrated access to a large collection of bibliographic citations, a full text document in cardiology, and a large thesaurus of medical terms. CODER is a distributed expert-based information system that incorporates techniques from artificial intelligence, information retrieval, and human-computer interaction to support effective access to information and knowledge bases. LEND is an object-oriented database which incorporates techniques from information retrieval and database systems to support complex objects, hypertext/hypermedia and semantic network operations efficiently with very large sets of data. LEND stores the CED lexicon, MeSH thesaurus, MEDLARS bibliographics records on cardiology, and the syllabus for the topic Abnormal Human Biology (Cardiology Section) taught at Columbia University. Together, CODER/LEND allow efficient and flexible access to all of this information while supporting rapid "intelligent" searching and hypertext-style browsing by both novice and expert users. This report gives statistics on the collections, illustrations of the system's use, and details on the overall architecture and design for Project INCARD