Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.

Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users' posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classification of multilingual data. Their comparison has unveiled insightful outcomes and has a twofold interpretation. Multilingual BERT-based classifiers achieve high performances and transfer inference when trained and fine-tuned on multilingual data. While also the zero-shot approach presents a novel technique for creating multilingual solutions in a faster, more efficient, and scalable way. It can easily be fitted to new languages and new tasks while achieving relatively good results across many languages. However, when efficiency and scalability are less important than accuracy, it seems that this model, and zero-shot models in general, can not be compared to fine-tuned and trained multilingual BERT-based classifiers.

Electronic Health Records at People's Hands Across Europe: The InteropEHRate Protocols.

Sharing of personal health data could facilitate and enhance the quality of care and the conduction of further research studies. However, these data are still underutilized due to legal, technical, and interoperability challenges, whereas the data subjects are not able to manage, interact, and decide on what to share, with whom, and for what purposes. This barrier obstacles continuity of care across in the European Union (EU), and neither healthcare providers nor data researchers nor the citizens are benefiting through efficient healthcare treatment and research. Despite several national-level EU studies and research activities, cross-border health data exchange and sharing is still a challenging task, which is addressed only under specific cases and scenarios. This manuscript presents the InteropEHRate research project along with its key innovations, aiming to offer Electronic Health Records (EHRs) at peoples' hands across the EU, via the exploitation of three (3) different protocol families, namely the Device-to-Device (D2D), Remote-to-Device (R2D), and Research Data Sharing (RDS) protocols. These protocols facilitate efficient, secure, privacy preserving, and General Data Protection Regulation (GDPR) compliant health data sharing across the EU, covering different real-world use cases.

A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.

Health information exchange through a Device-to-Device protocol supporting lossless encoding and decoding.

Despite the availability of secure electronic data transfers, most medical information is still stored on paper, and it is usually shared by mail, fax or the patients themselves. Today's technologies aim to the challenge of sharing healthcare information, since exchanging inaccurate data leads to inefficiency and errors. Currently, there exist numerous techniques for exchanging data, which however require continuous internet connection, thus lacking generic applicability in healthcare, in the cases where no internet connection is available. In this paper, a new Device-to-Device (D2D) protocol is proposed, specifying a series of Bluetooth messages regarding the healthcare information that is being exchanged in short-range distances, between a healthcare-practitioner and a citizen. This information refers to structured and unstructured data, which can be directly exchanged through a globally used communication protocol, extending it for the permission of exchanging HL7 FHIR Bluetooth structured messages. Moreover, for high volume data, the D2D protocol can support lossless compression and decompression, improving its overall efficiency. The protocol is firstly evaluated through exchanging sample data in a real-world scenario, whereas an overall comparison of exchanging multiple sized data either using lossless compression or not is being provided. According to the evaluation results, the D2D protocol specification was strictly followed, successfully providing the ability to exchange healthcare-related data, with Bluetooth being considered the most suitable technology for current needs. For small-sized data, the D2D protocol performs better without the provided lossless compression mechanism, whereas in the case of large-sized data lossless compression is considered as the only option.

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics.

Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema's applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.

Ontology-Driven Data Cleaning Towards Lossless Data Compression.

With the available data in healthcare, healthcare organizations and practitioners require interoperable, efficient, and non-time-consuming data exchange. Currently, several cases aim to the exchanged data security, without considering the complexity of the data to be exchanged. This paper provides an Ontology-driven Data Cleaning mechanism, facilitating Lossless Healthcare Data Compression to efficiently compress healthcare data of different nature (textual, audio, image). The latter is being evaluated considering three datasets of different formats, concluding to the added value of the described mechanism.

Adjustable Data Cleaning Towards Extracting Statistical Information.

Each device, organization, or human, is affected by the effects of Big Data. Analysing these vast amounts of data can be considered of vital importance, surrounded by many challenges. To address a portion of these challenges, a Data Cleaning approach is being proposed, designed to filter the non-important data. The functionality of the Data Cleaning is evaluated on top of Global Terrorism Data, to furtherly create policies on how terrorism is affecting national healthcare.

A Secure Protocol for Managing and Sharing Personal Healthcare Data.

Current technologies provide the ability to healthcare practitioners and citizens, to share and analyse healthcare information, thus improving the patient care quality. Nevertheless, European Union (EU) citizens have very limited control over their own health data, despite that several countries are using national or regional Electronic Health Records (EHRs) for realizing virtual or centralized national repositories of citizens' health records. Health Information Exchange (HIE) can greatly improve the completeness of patients' records. However, most of the current researches deal with exchanging health information among healthcare organizations, without giving the ability to the citizens on accessing, managing or exchanging healthcare data with healthcare organizations and thus being able to handle their own data, mainly due to lack of standardization and security protocols. Towards this challenge, in this paper a secure Device-to-Device (D2D) protocol is specified that can be used by software applications, aiming on facilitating the exchange of health data among citizens and healthcare professionals, on top of Bluetooth technologies.

Health Record Index: Secure Access of Cloud-Stored Healthcare Data.

Healthcare 4.0 demands healthcare data to be shaped into a common standardized and interoperable format for achieving more efficient data exchange. What is also needed is for this healthcare data to be both easily stored and securely accessed from anywhere, and vice versa. Currently, this is achieved through the secure storage of the healthcare data in different cloud repositories and infrastructures, which however increase the difficulty of accessing it in emergency situations from healthcare practitioners, or even from the citizens' themselves. The latter need to have specific credentials for accessing healthcare data in private cloud repositories, which can be almost impossible in urgent situations where this data must be accessed no matter what. For that reason, in this paper we are proposing a new health record indexing methodology that facilitates the access of non-privileged users (e.g. healthcare practitioners), to the healthcare data stored in cloud repositories of citizens-in-need, under the circumstances of emergency cases.

A Semantic Similarity Evaluation for Healthcare Ontologies Matching to HL7 FHIR Resources.

Healthcare 4.0 demands healthcare data to be shaped into a common standardized and interoperable format for achieving more efficient data exchange. Most of the techniques addressing this domain are dealing only with specific cases of data transformation through the translation of healthcare data into ontologies, which usually result in clinical misinterpretations. Currently, ontology alignment techniques are used to match different ontologies based on specific string and semantic similarity metrics, where very little systematic analysis has been performed on which semantic similarity techniques behave better. For that reason, in this paper we are investigating on finding the most efficient semantic similarity technique, based on an existing approach that can transform any healthcare dataset into HL7 FHIR, through the translation of the latter into ontologies, and their matching based on syntactic and semantic similarities.

Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching.

BACKGROUND AND OBJECTIVE: Healthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and human interaction fields. Currently, the increasing availability of health data is demanding data-driven approaches, bringing the opportunities to automate healthcare related tasks, providing better disease detection, more accurate prognosis, faster clinical research advance and better fit for patient management. In order to share data with as many stakeholders as possible, interoperability is the only sustainable way for letting systems to talk with one another and getting the complete image of a patient. Thus, it becomes clear that an efficient solution in the data exchange incompatibility is of extreme importance. Consequently, interoperability can develop a communication framework between non-communicable systems, which can be achieved through transforming healthcare data into ontologies. However, the multidimensionality of healthcare domain and the way that is conceptualized, results in the creation of different ontologies with contradicting or overlapping parts. Thus, an effective solution to this problem is the development of methods for finding matches among the various components of ontologies in healthcare, in order to facilitate semantic interoperability. METHODS: The proposed mechanism promises healthcare interoperability through the transformation of healthcare data into the corresponding HL7 FHIR structure. In more detail, it aims at building ontologies of healthcare data, which are later stored into a triplestore. Afterwards, for each constructed ontology the syntactic and semantic similarities with the various HL7 FHIR Resources ontologies are calculated, based on their Levenshtein distance and their semantic fingerprints accordingly. Henceforth, after the aggregation of these results, the matching to the HL7 FHIR Resources takes place, translating the healthcare data into a widely adopted medical standard. RESULTS: Through the derived results it can be seen that there exist cases that an ontology has been matched to a specific HL7 FHIR Resource due to its syntactic similarity, whereas the same ontology has been matched to a different HL7 FHIR Resource due to its semantic similarity. Nevertheless, the developed mechanism performed well since its matching results had exact match with the manual ontology matching results, which are considered as a reference value of high quality and accuracy. Moreover, in order to furtherly investigate the quality of the developed mechanism, it was also evaluated through its comparison with the Alignment API, as well as the non-dominated sorting genetic algorithm (NSGA-III) which provide ontology alignment. In both cases, the results of all the different implementations were almost identical, proving the developed mechanism's high efficiency, whereas through the comparison with the NSGA-III algorithm, it was observed that the developed mechanism needs additional improvements, through a potential adoption of the NSGA-III technique. CONCLUSIONS: The developed mechanism creates new opportunities in conquering the field of healthcare interoperability. However, according to the mechanism's evaluation results, it is almost impossible to create syntactic or semantic patterns for understanding the nature of a healthcare dataset. Hence, additional work should be performed in evaluating the developed mechanism, and updating it with respect to the results that will derive from its comparison with similar ontology matching mechanisms and data of multiple nature.

Analyzing data and data sources towards a unified approach for ensuring end-to-end data and data sources quality in healthcare 4.0.

BACKGROUND AND OBJECTIVE: Healthcare 4.0 is being hailed as the current industrial revolution in the healthcare domain, dealing with billions of heterogeneous IoT data sources that are connected over the Internet and aim at providing real-time health-related information for citizens and patients. It is of major importance to utilize an automated way to identify the quality levels of these data sources, in order to obtain reliable health data. METHODS: In this manuscript, we demonstrate an innovative mechanism for assessing the quality of various datasets in correlation with the quality of the corresponding data sources. For that purpose, the mechanism follows a 5-stepped approach through which the available data sources are detected, identified and connected to health platforms, where finally their data is gathered. Once the data is obtained, the mechanism cleans it and correlates it with the quality measurements that are captured from each different data source, in order to finally decide whether these data sources are being characterized as qualitative or not, and thus their data is kept for further analysis. RESULTS: The proposed mechanism is evaluated through an experiment using a sample of 18 existing heterogeneous medical data sources. Based on the captured results, we were able to identify a data source of unknown type, recognizing that it was a body weight scale. Afterwards, we were able to find out that the API method that was responsible for gathering data out of this data source was the getMeasurements() method, while combining both the body weight scale's quality and its derived data quality, we could decide that this data source was considered as qualitative enough. CONCLUSIONS: By taking full advantage of capturing the quality of a data source through measuring and correlating both the data source's quality itself and the quality of its derived data, the proposed mechanism provides efficient results, being able to ensure end-to-end both data sources and data quality.

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices.

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices' datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

CrowdHEALTH: Big Data Analytics and Holistic Health Records.

The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.

Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment.

Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data' diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards' requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism.

Interoperability Techniques in CrowdHEALTH project: The Terminology Service.

INTRODUCTION: Healthcare information systems' (HIS) lack of interoperability remains a challenge and a barrier for important health-related events detection. While relevant techniques are based on medical standards and technologies, these techniques do not follow a holistic approach. The creation of a set of tools that fulfils the needs of interoperability is needed. AIM: The aim of this paper is to present the terminology service envisioned while defining the initial design of the Interoperability solution proposed for the CrowdHEALTH project. METHODS: In the CrowdHEALTH project, specific subcomponents responsible for providing the appropriate functionalities have been designed: The rule engine for the implementation of the business logic, the Structure Mapping Service which is responsible for creating and managing the knowledge related to the link that exists between information structures, or mappings between them and the Terminology Service for providing a set of operations on medical terminologies used for the coding of medical knowledge, which fill the information structures. RESULTS: Therefore, it is possible to provide a series of functionalities about these information elements found within more complex structures expressed in a local code and translated into other standardized medical terminology. Towards this end, CrowdHEALTH presents the terminology service envisioned in the context of the initial design of the interoperability solution. CONCLUSION: CrowdHEALTH project provides an infrastructure to convert the clinical information into meaningful data so that healthcare systems communicate effectively. This initial proposal will be further extended and tested during the project life circle.

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

INTRODUCTION: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. AIM: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. METHODS: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. RESULTS: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. CONCLUSIONS: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

Matching Ontologies to HL7 FHIR Towards Their Syntactic and Semantic Similarity.

Current medical systems need to be able to communicate complex and detailed medical data securely and efficiently. However, the quantity of available healthcare data is rising rapidly, far exceeding the capacity to deliver personal or public health benefits from analyzing this data. Thus, a substantial overhaul of methodology is required to address the real complexity of health. This can be achieved by constructing medical domain ontologies for representing medical terminologies, considered to be a difficult task, requiring a profound analysis of the structure and the concepts of medical terminologies. In this paper, a mechanism is presented for constructing healthcare ontologies, while matching them to HL7 FHIR Resources ontologies both in terms of syntactic and semantic similarity, in order to understand their nature and translate them into a common standard to improve the quality of patient care, research, and health service management.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.