From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer

Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios. A challenging scenarios for artificial intelligence (AI) is using biomedical data (e.g., texts, imaging, omics, and clinical) to provide diagnosis and treatment recommendations for cancerous conditions.~Data and knowledge about biomedical entities like cancer, drugs, genes, proteins, and their mechanism is spread across structured (knowledge bases (KBs)) and unstructured (e.g., scientific articles) sources. A large-scale knowledge graph (KG) can be constructed by integrating and extracting facts about semantically interrelated entities and relations. Such a KG not only allows exploration and question answering (QA) but also enables domain experts to deduce new knowledge. However, exploring and querying large-scale KGs is tedious for non-domain users due to their lack of understanding of the data assets and semantic technologies. In this paper, we develop a domain KG to leverage cancer-specific biomarker discovery and interactive QA. For this, we constructed a domain ontology called OncoNet Ontology (ONO), which enables semantic reasoning for validating gene-disease (different types of cancer) relations. The KG is further enriched by harmonizing the ONO, metadata, controlled vocabularies, and biomedical concepts from scientific articles by employing BioBERT- and SciBERT-based information extractors. Further, since the biomedical domain is evolving, where new findings often replace old ones, without having access to up-to-date scientific findings, there is a high chance an AI system exhibits concept drift while providing diagnosis and treatment. Therefore, we fine-tune the KG using large language models (LLMs) based on more recent articles and KBs.Comment: arXiv admin note: substantial text overlap with arXiv:2302.0473

Meme kanseri hastalarında bilgi teknolojileri ile güçlendirilmiş hasta modelinin etkilerinin sorgulanması : Türkiye'den bir örnek.

This thesis aims to examine how patient empowerment based on Internet information has impact on health care processes and patient – physician relationship. The process of empowerment is analyzed in three main steps; searching and obtaining information; sharing and discussing obtained information with providers; and involving decision making process. Study domain covers with breast cancer patients continuing their treatment in hospitals. In-depth interview methodology has been employed. Interviews are conducted in two settings: one is a university hospital; other is a state hospital of Ministry of Health. Sample size was 20 patients and 6 doctors. As result of study we observed that most of the breast cancer patients have low level of empowerment. This is mainly caused by perception of cancer and high level of anxiety of patients. Most of the middle class women even though they use Internet in everyday life, they neither want to search for information on their cancer not they want to involve in decision making.. Some of the educated upper middle class use Internet however they do not share gathered information with their doctors. They mainly use this information to test competency of doctor. Most patients prefer to seek for information until they made a decision, mostly finding a trustable doctor. Even though some of upper middle class, high education women use Internet intensively, they do not involve in decision and leave the responsibility to the doctor. Young generation regardless of their socio economic situation has tendency to use internet and getting empowered.M.S. - Master of Scienc

Sağlık bakımında performans ölçümü için yeni bir ontoloji ve bilgi tabanı sistemi.

Performance measurement makes up the core of all health care systems in transition. Many countries and institutions monitor different aspects of health care delivery systems for differing purposes. Health care deliverers are compared, rated, and given incentives with respect to their measured performance. However, a global health care domain is currently striving for attaining commonly accepted performance measurement models and base-standards that can be used in information systems. The objective of this thesis is to develop an ontological framework to represent performance measurement and apply this framework to interpret performance measurement studies semantically. More specifically, this study made use of a formal ontology development methodology by utilizing web ontology and semantic web rule languages with description logic in order to develop a commonly accepted health care performance measurement ontology and knowledge base system. In the ontology developed, dimensions, classes, attributes, rules and relationships used in health care delivery and performance measurement domain are defined while forming an initial knowledge base for performance measurement studies and indicators. Furthermore, we applied the developed performance measurement ontology to the knowledge base while driving those related performance indicators for predefined categories. The ontology is evaluated within the features of the Turkish health care system. Health care deliverer categories are identified and by executing inference rules on the knowledge base, related indicators are retrieved. Results are evaluated by domain experts coming from regulatory and care provider institutions. The major benefit of the developed ontology is that it presents a sharable and extensible knowledge base that can be used in the newly emerging performance measurement domain. Moreover, this conceptualization and knowledge base system serve as a semantic indicator search tool that can be used in different health care settings.Ph.D. - Doctoral Progra

A Framework for Applying Data Integration and Curation Pipelines to Support Integration of Migrants and Refugees in Europe

We investigate the benefit of data integration and curation services for the current refugee crisis and proposed an architecture to support development of innovative solutions. We focus on developing a multi- /cross-lingual semantic data curation pipeline enriched with natural language processing capabilities in order to (a) improve decision making capabilities of public authorities with data driven dashboards; (b) stimulate the development of innovative application and services supporting integration of refugees; and (c) improve the use of open data for tackling the societal challenges

Mining Maximal Frequent Patterns in Transactional Databases and Dynamic Data Streams: A Spark-based Approach

Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysis (MBA). Although, numerous studies have been carried out in this area, most of them extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some existing approaches degrade drastically due to the presence of null transactions. We, therefore, proposed an efficient way to mining MFPs with Apache Spark to overcome these issues. For the faster computation and efficient utilization of memory, we utilized a prime number based data transformation technique, in which values of individual transaction have been preserved. After removing null transactions and infrequent items, the resulting transformed dataset becomes denser compared to the original distributions. We tested our proposed algorithms in both real static TDBs and DDSs. Experimental results and performance analysis show that our approach is efficient and scalable to large dataset sizespeerReviewe

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data : are we ready from an international perspective?

Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)-based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems.Key Points• Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration.• The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems.• While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns