Stakeholders of Cardiovascular Innovation Ecosystems in Germany: A First Level Analysis and an Example

This paper aims to provide a first attempt towards analysis innovation ecosystems for cardiovascular pathologies in Germany through the use of a stakeholder model. We present essential stakeholders for the development and deployment of innovations in the field of cardiovascular research and medicine, and the primary functions they fulfill in the context of these innovation ecosystems. The adopted approach consists of the implementation of a multilevel system model for analyzing stakeholders in this particular field. Data acquisition transpired through systematic literature review of multiple articles and studies. Data analysis phases were executed until reaching a point at which the considerable amount of data was discovered, ensuring consistency across various sources. We demonstrate that innovation ecosystems in cardiovascular medicine involve interconnected networks of stakeholders across different fields. Moreover, through an investigation of innovation ecosystems of cardiovascular pathologies particularly in Germany, we present the functions undertaken by each stakeholder, which are essential for the participation in the innovation ecosystems. The findings presented in this paper hold the potential to bring better understanding of cardiovascular pathology innovation ecosystems in Germany. This assertion is substantiated through a comprehensive examination of relevant scientific literature

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

Explainable AI for Bioinformatics: Methods, Tools, and Applications

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness

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