BackgroundThere is a strong need for preventive approaches to reduce the incidence of recurrence, second cancers, and late toxicities in head and neck squamous cell carcinoma (HNSCC) survivors. We conducted a randomized controlled trial (RCT) to assess a dietary intervention as a non-expensive and non-toxic method of tertiary prevention in HNSCC survivors.MethodsEligible participants were disease-free patients with HNSCC in follow-up after curative treatments. Subjects were randomized 1:1 to receive a highly monitored dietary intervention plus the Word Cancer Research Fund/American Institute for Cancer Research recommendations for cancer prevention (intervention arm) or standard-of-care recommendations (control arm). The planned sample size for the event-free survival evaluation (primary endpoint) was not reached, and the protocol was amended in order to investigate the clinical (nutritional and quality-of-life questionnaires) and translational study [plasma-circulating food-related microRNAs (miRNAs)] as main endpoints, the results of which are reported herein.ResultsOne hundred patients were screened, 94 were randomized, and 89 were eligible for intention-to-treat analysis. Median event-free survival was not reached in both arms. After 18 months, nutritional questionnaires showed a significant increase in Recommended Food Score (p = 0.04) in the intervention arm vs. control arm. The frequency of patients with and without a clinically meaningful deterioration or improvement of the C30 global health status in the two study arms was similar. Food-derived circulating miRNAs were identified in plasma samples at baseline, with a significant difference among countries.ConclusionThis RCT represented the first proof-of-principle study, indicating the feasibility of a clinical study based on nutritional and lifestyle interventions in HNSCC survivors. Subjects receiving specific counseling increased the consumption of the recommended foods, but no relevant changes in quality of life were recorded between the two study arms. Food-derived plasma miRNA might be considered promising circulating dietary biomarkers.

Healthcare services and products are rapidly changing due to the development of new technologies, offering relevant solutions to improve patient outcomes. Patient-Generated Health Data and knowledge-sharing across the European Union (EU) has a great potential of making healthcare provision more effective and efficient by putting the patient at the centre of the healthcare process. While such initiatives have been taken before, a uniting and overarching approach is still missing. The EU-funded IMPROVE project will develop an evidence-based and actual framework to effectively leverage the added value of people-centred integrated healthcare solutions, using predominantly PROMs, PPI, PREMs, and other Patient-Generated Health Data (PGHD). As a result, the project facilitates the effective and efficient implementation of Value-Based Healthcare across the EU by putting the patient central in the healthcare process.

We describe the AI-ARC (Artificial Intelligence-based Virtual Control Room for the Arctic) system, which aims to enhance maritime domain awareness and surveillance. The system is micro-service based and fuses data from various sources, utilizing AI-driven micro-services and an advanced visualization platform to increase the situation awareness of maritime surveillance operators. The results of the Baltic sea demonstration, aiding in the detection of illegal activities, environmental protection, are presented. The system was evaluated using historical data from real criminal incidents. The resultsshow that the AI-ARC approach could help increase the situation awareness of law enforcement operators.

AbstractHead and neck squamous cell carcinomas (HNSCC) are aggressive and heterogenous tumors with a high fatality rate. Many gene signatures (GS) have been developed with both prognostic and predictive significance. We aimed to externally validate five published GS in a large European collection of HNSCC patients. Gene expression from 1097 treatment-naïve HNSCC patients’ primary tumors was used to calculate scores for the five GS. Cox proportional hazard models were used to test the association between both 2-year overall survival and 2-year disease-free survival and the signature scores. The predictive role of GS was validated by comparing survival associations in patients receiving specific treatment (i.e., radiotherapy, systemic treatment) versus those who did not. We successfully externally validated all 5 GS, including two prognostic signatures, one signature as prognostic and predictive of sensitivity to systemic treatment, while signatures for cisplatin-sensitivity and radiosensitivity were validated as prognostic only.

Background: Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, which motivates the construction of a high-quality harmonization model to standardize this multi-source clinical data in terms of format and semantics. The use of ontologies and semantic techniques is a well-known approach to face this challenge.Objective: This work aims to provide a clinically reliable data model for HNC processes during all phases of the disease: prognosis, treatment, and follow-up. Therefore, we built the first ontology specifically focused on the HNC domain, named HeNeCOn (Head and Neck Cancer Ontology).Methods: First, an annotated dataset was established to provide a formal reference description of HNC. Then, 170 clinical variables were organized into a taxonomy, and later expanded and mapped to formalize and integrate multiple databases into the HeNeCOn ontology. The outcomes of this iterative process were reviewed and validated by clinicians and statisticians.Results: HeNeCOn is an ontology consisting of 502 classes, a taxonomy with a hierarchical structure, semantic definitions of 283 medical terms and detailed relations between them, which can be used as a tool for information extraction and knowledge management.Conclusion: HeNeCOn is a reusable, extendible and standardized ontology which establishes a reference data model for terminology structure and standard definitions in the Head and Neck Cancer domain. This ontology allows handling both current and newly generated knowledge in Head and Neck cancer research, by means of data linking and mapping with other public ontologies.

Age-related changes in pharmacokinetics and pharmacodynamics, multimorbidity, frailty, and cognitive impairment represent challenges for drug treatments. Moreover, older adults are commonly exposed to polypharmacy, leading to increased risk of drug interactions and related adverse events, and higher costs for the healthcare systems. Thus, the complex task of prescribing medications to older polymedicated patients encourages the use of Clinical Decision Support Systems (CDSS). This paper evaluates the CDSS miniQ for identifying potentially inappropriate prescribing in poly-medicated older adults and assesses the usability and acceptability of the system in health care professionals, patients, and caregivers. The results of the study demonstrate that the miniQ system was useful for Primary Care physicians in significantly improving prescription, thereby reducing potentially inappropriate medication prescriptions for elderly patients. Additionally, the system was found to be beneficial for patients and their caregivers in understanding their medications, as well as usable and acceptable among healthcare professionals, patients, and caregivers, highlighting the potential to improve the prescription process and reduce errors, and enhancing the quality of care for elderly patients with polypharmacy, reducing adverse drug events, and improving medication management.

As survivorship chances for cancer improve, the necessity to properly manage the quality of life post-treatment increases. Head and Neck Cancer is one of the most prevalent ones (being the seventh most common cancer in the world). In this paper we introduce the BD4QoL Ontology, which provides a comprehensive and integrated data model for HN cancer survivors. The presented ontology models several relevant areas of the knowledge domain: the patients clinical and demographic data, the questionnaires commonly used to ascertain their QoL and the related behavioral and emotional traits that can be used to infer the QoL.

Cancer-related mortality has been decreasing in the last years, meaning that over 50% adult cancer patients are expected to survive for at least five years. Managing follow-up (FU) guidelines is a critical and indispensable activity in oncology. Timing and precision in the decision-making process impact on the long-term healthiness of cancer patients. In this sense, information and communication technologies (ICT) are essential to support clinicians in the cancer patients´ FU. This work presents the outcomes from a research work conducted for Point of Care (PoC) systems applied in cancer FU. The CeHRes (Center for eHealth Research and Disease Management) roadmap has been applied. From this, we conducted the first phase named Contextual inquiry: (1) a scoping review was carried out on the existing solutions following PRISMA guidelines; (2) recommendations from 7 experts (oncologists, technician, psychologist, epidemiologist) have been analyzed; (3) market research has been conducted on existing PoC systems and tools within eHealth market; and (4) the stakeholders have been identified by analyzing the results from the first 3 steps. A survey has been designed to verify and understand these results. The structure of the form includes i) explanation of the goal and methods; ii) profile questions; iii) questions to confirm the results. The Contextual inquiry results with around 30% of tools dedicated only to the FU phase. Regarding technology, there is a trend in the development of web-based solutions, 52% of tools analyzed are web-based. The tools generally include features such as dashboards, decision making support, data exploration and electronic health record integration. 13 stakeholders were identified, being the main beneficiaries clinicians, patients, nurses and hospital staff. This work shows currently available ICT options to support clinical practice, and reveals actual needs. The results show the lack of specialized FU tools. Besides, very few of them include a system of alerts or the possibility of customizing a personalized FU. Also, tailoring data visualization is missing in most of the existing tools. The survey would verify the results and the unmet needs identified in this work.

Head and Neck Cancer is the seventh cancer in incidence worldwide and this high mortality is due to the major cases are diagnosed in advanced stages. Currently, the selection of treatment is based on the Tumor-lymph-Nodes-Metastasis prognostic system. This system only considers a few risk factors, being inadequate due to the heterogeneity of such tumors. Within BD2Decide project, an Integrated Decision Support System is being implemented to link data coming from different disciplines with the purpose of providing the necessary information to tailor treatment and care delivery pathways to each Head and Neck Cancer patient. A clinical study with more than 1000 of patients is used to validate the system.

Head and Neck Cancer, the seventh cancer in incidence worldwide, is a heterogeneous disease that encompasses different molecular entities and subgroups with variable risk and potential discriminative treatment options that influence disease outcome. Treatment choice depends mainly on a staging system that has limits on advances cases (Stages III and IV). Because of that, it is needed to find more prognostic factors that can enhance this current classification. Population data contribute on the identification of risk and prognostic factors. Therefore, in the era of precision medicine, the integration of population data with patient clinical data is expected to contribute to a better patient stratification. In this work, carried out in the context of the European Research project "Big Data to Decide" (BD2Decide), the use of publicly available data sources to improve decision making in Head and Neck Cancer, and their integration in a computerized decision support system have been studied with the contribution of oncologists, epidemiologists and bio-statisticians. The conceptual framework design presented in this paper pretends to support the discovery of prognostic factors, improving risk stratification in Head and Neck Cancer.

Head and Neck Cancer (HNC) is one of the cancers with the highest mortality and recurrence rates. Nowadays, HNC research is focused on enhancing the prognostic and quality of life of patients. This disease involves heterogeneous and multiscale data that should be integrated and analyzed during the process of diagnosis, prognosis and treatment of HNC. In this work, we propose a solution capable of integrating all this data, providing a standardized vocabulary of terms involved during the HNC research process. The solution is based on the creation of the first ontology that models the HNC disease and collects and organize hierarchically, not only the data at patient level, but also population data and concepts related to clinical and scientific literature.

The main goal of this work is to understand and design a workflow on the way to integrate radiomics, a promising technique that extract clinical images features, in computerized Decision Support Systems, with meaningful representation of this information to healthcare professionals.

This article describes the procedure of definition and design of a process for the continuity care unit to improve the attention to the patient and his/her ecosystem providing a novel alternative to the conventional methods. This work was done under the framework of the MiniQ project, funded by EIT Health to improve the management of poly-medicated patients.

Head and Neck Cancer (H&NC) is one of the most complex and difficult cancers in terms of treatment and prognosis. Decision Support Systems (DSS) are currently used in several aspects of the clinical practice, however in few cases they have been used to improve decision making process of H&NC. In this paper we propose a DSS conceptual architecture, which allow to use both traditional methods (e.g. clinical guidelines and survival analyses) and sophisticated techniques (e.g. computer models for prediction and knowledge discovery).

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems.

BACKGROUND:Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability.METHODS:The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients.RESULTS:The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach.CONCLUSIONS:Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.

Heuristic Evaluation (HE) is a well-known method for usability evaluation. HE allows the identification of the most generic and severe usability issues with a reduced effort and very quickly. This approach allows the quick iteration of prototypes and the update of the usability of the system before to run a usability study with the final users. This work reports the HE carried out with different tools built for the efficient management of Type 2 Diabetes Mellitus (T2DM)

Type 2 Diabetes screening and risk stratification tools could benefit from the incorporation of predictive systems based on computer modelling. The adoption of User Centered Design techniques is fundamental in order to integrate these systems in an effective and successful way. The work presented in this paper describe the methodologies used in the context of a multidisciplinary research project and provides an overview of the preliminary results.

The emerging of new Information and Communications Technologies (ICT), the aging population and the increased number of people suffering from chronic diseases are changing the health structure of developed countries. Given this situation, it is crucial that research based on demographics data is promoted and researchers can access large amounts of patient data, having this information validated by medical institutions. Currently, there are some platforms and web applications available that allow patients the self-management and control of their health and wellness information. Other platforms allow managing large amounts of patient data (demographic, diagnostic, laboratory and medication) and share patient cohorts considering different criteria of inclusion and exclusion. This paper proposes to use a selection of these platforms and it defines how to relate them to obtain a framework where the information provided by patients and medical institutions can be safely used and validated by researchers. Furthermore, the system will enable clinical researcher to share the results of their research in a controlled and safe environment.