A5049001331- Data Quality and Management
- Artificial Intelligence in Healthcare
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Big Data and Business Intelligence
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- Pancreatic and Hepatic Oncology Research
- IoT and Edge/Fog Computing
- Data Mining Algorithms and Applications
- Text and Document Classification Technologies
- Caching and Content Delivery
- Privacy-Preserving Technologies in Data
- Explainable Artificial Intelligence (XAI)
- Cloud Computing and Resource Management
- Ethics and Social Impacts of AI
- AI in cancer detection
- Virtual Reality Applications and Impacts
- Software System Performance and Reliability
- Simulation-Based Education in Healthcare
- Computational and Text Analysis Methods
- Art History and Market Analysis
- Economic and Financial Impacts of Cancer
- Empathy and Medical Education
- Natural Language Processing Techniques
University of Piraeus
2020-2025
Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented utilized in different domains, such as health care policy making. At same time, tremendous growth popularity usage social media, Twitter, has resulted on an immense increase user-generated data, mainly represented by corresponding texts users' posts. However, these specific data extraction actionable knowledge added value out them is a...
The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT sources and Electronic Health Record (EHR) systems. Based on the advancements in science Machine Learning (ML), an improved ability to integrate process so-called primary secondary fosters provision of real-time personalized decisions. In that direction, innovative mechanism for processing integrating health-related introduced this article. It describes details its internal subcomponents workflows, together...
As Artificial Intelligence (AI) becomes more integrated into public governance, concerns about its transparency and accountability have become increasingly important. The use of AI in decision-making processes raises questions bias, fairness, the protection individual fundamental rights. To ensure that is used a way upholds democratic values, it essential to develop systems are trustworthy, transparent, accountable. Trusted allows citizens greater trust organizations their processes, while...
The adoption of the European Health Data Space (EHDS) regulation has made integrating health data critical for both primary and secondary applications. Primary use cases include patient diagnosis, prognosis, treatment, while applications support research, innovation, regulatory decision-making. Additionally, leveraging large datasets improves training quality artificial intelligence (AI) models, particularly in cancer prevention, prediction, treatment personalization. Union (EU) recently...
We are creating a synergy among European Health Data Space projects (e.g., IDERHA, EUCAIM, ASCAPE, iHELP, Bigpicture, and HealthData@EU pilot project) via health standards usage thanks to the HSBOOSTER EU Project since they involved or using standards, and/or designing ontologies. compare health-standardized models/ontologies/terminologies such as HL7 FHIR, DICOM, OMOP, ISO TC 215 Informatics, W3C DCAT, etc. used in those projects.
Abstract We present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation management policy-relevant dataflows, by enabling the practitioner to register datasets specify sequence transformations and/or information extraction through registered ingest functions. Once possibly transformed dataset has been ingested, additional insights can be retrieved further applying...
Edge computing has become a prominent solution when it comes to mobile applications and data management, due its ability considerably reduce transmission costs, analyze requiring fewer resources, since the analysis occurs at lower volumes, without having relocate centralized infrastructures. One major challenge, indicates optimal placement regarding data-intensive and, in general, vast of large amount data. In this paper, we propose novel user mobility-based strategy, considering trade-off...
Cloud computing has become the norm when it comes to web applications deployment over last years. Yet, data-intensive have shown a great growth were more and are being developed on daily basis. Moreover, edge devices gained boost in performance, allowing them manage analyze data very efficiently, thus making an equally as effective alternative. Taking into consideration always increasing number of information that is generated requires enormous amount resources, hybrid cloud /...
Scientific and clinical research have advanced the ability of healthcare professionals to more precisely define diseases classify patients into different groups based on their likelihood responding a given treatment, future risks. However, significant gap remains between delivery stratified personalization. The latter implies solutions that seek treat each citizen as truly unique individual, opposed member group with whom they share common risks or health-related characteristics....
Given the challenge that healthcare related data are being obtained from various sources and in divergent formats there is an emerging need for providing improved automated techniques technologies perform qualification standardization of these data. The approach presented this paper introduces a novel mechanism cleaning, qualification, collected primary secondary types. latter realized through design implementation three (3) integrated subcomponents, Data Cleaner, Qualifier, Harmonizer...
The tremendous growth, popularity, and usage of social media in modern societies has led to the production an enormous real-time volume texts posts, including Tweets that are being produced by users. These collections data can be potentially useful, but extent meaningful these is still high research business interest. One main elements several application domains, such as policy making, addresses scope identifying categorizing into natural groups based on topics which they refer to, order...
One of the main elements in several application domains, such as policy making, addresses scope public opinion analysis. The latter is recently realized through sentiment analysis and Natural Language Processing, for identifying extracting subjective information from raw texts. An additional challenge refers to exploitation correlation data different languages, order analyse sentiments by considering all available a holistic way. This paper investigates impact Neural Machine Translation...
Nowadays organizations are surrounded with enormous amounts of data, losing all the important information that resides in it. Knowledge Discovery Databases (KDD) can aid to transform this data into valuable information, by extracting complex patterns and relationships from To achieve that, various KDD techniques tools have been proposed, resulting impressive outcomes domains, especially healthcare. Due huge amount available within healthcare systems, mining is extremely for sector. However,...
The tremendous growth and usage of social media in modern societies have led to the production an enormous real-time volume texts posts, including tweets, that are being produced by users. These collections data can be potentially useful, but extent meaningful these is still high research business interest. One main elements several application domains, such as policy making, addresses scope public opinion analysis. latter recently realized through sentiment analysis Natural Language...
In recent years, vast amounts of data are generated from a plethora devices, systems, and platforms, covering wide range domains. This increase generates the necessity to access technical technological resources enabling efficient ready-to-use analysis solutions, including for training learning in addition infrastructure elements, as well Artificial Intelligence (AI)/Machine Learning (ML) techniques. Current solutions siloed, instead being structured, integrated, openly accessible...
<sec> <title>UNSTRUCTURED</title> The adoption of the European Health Data Space (EHDS) regulation has made integrating health data critical for both primary and secondary applications. Primary use cases include patient diagnosis, prognosis, treatment, while applications support research, innovation, regulatory decision-making. Additionally, leveraging large datasets improves training quality artificial intelligence (AI) models, particularly in cancer prevention, prediction, treatment...