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Wearable real-time systems collecting and smartly analyzing information about patient health status could help medical personnel adopting the most suitable countermeasures in case of highly stressful situations in military and civil scenarios. Such situations include terrorist attacks or rescue operations. We propose the design and development of a patch-like device prototype (SmartPatch) and a methodology enabling continuous evaluation of victims' vital parameters. Using this innovative platform after the first triage, the onsite emergency teams will have continuous information about the health status of each person wearing the SmartPatch. If the health status of a victim is changed, SmartPatch is able to generate an alert and prevent overlook of critical health changes causing potential severe life-threatening consequences or death.

While our knowledge on the occurrence of rationed and missed nursing care and its associations with patient and nurse outcomes has increased, we still know very little on how we can effectively reduce and limit the level of rationed and missed nursing care. There are several interventions reported in the literature (e.g., staff planning and teamwork development) that have the potential to reduce and limit rationed and missed nursing care, as well as positively influence the performance of selected nursing care activities. Yet, the number of studies and the level of evidence is low. There are currently no studies dealing with interventions focused on safety climate, ethical climate, or nurse education and experience, all of which might offer a potential benefit as they have shown to predict rationed and missed nursing care. Moreover, there is a lack of studies on the use of technology (e.g., telehealth, robotics) to support nurses, e.g., in the process of decision-making and priority setting. Implementation science could provide guidance on the development, implementation, and evaluation of effective interventions to reduce and limit rationed and missed nursing care and help tackle some of the methodological challenges to generate the needed evidence to understand, when, why, and how such interventions work. The aim of this chapter is to provide a synthesis of the literature and give some future perspectives on interventions that may reduce or limit rationed and missed nursing care.

BACKGROUND This study examines the development of the connected health (CH) research landscape with a view to providing an overview of the existing CH research. The research field of CH has experienced rapid growth coinciding with increasing pressure on health care systems to become more proactive and patient centered. OBJECTIVE This study aimed to assess the extent and coverage of the current body of knowledge in CH. In doing so, we sought to identify specific topics that have drawn the attention of CH researchers and to identify research gaps, in particular those offering opportunities for further interdisciplinary research. METHODS A systematic mapping study that combined scientific contributions from research in the disciplines of medicine, business, computer science, and engineering was used. Overall, seven classification criteria were used to analyze the papers, including publication source, publication year, research type, empirical type, contribution type, research topic, and the medical condition studied. RESULTS The search resulted in 208 papers that were analyzed by a multidisciplinary group of researchers. The results indicated a slow start for CH research but showed a more recent steady upswing since 2013. The majority of papers proposed health care solutions (77/208, 37.0%) or evaluated CH approaches (49/208, 23.5%). Case studies (59/208, 28.3%) and experiments (55/208, 26.4%) were the most popular forms of scientific validation used. Diabetes, cancer, multiple sclerosis, and heart conditions were among the most prevalent medical conditions studied. CONCLUSIONS We conclude that CH research has become an established field of research that has grown over the last five years. The results of this study indicate a focus on technology-driven research with a strong contribution from medicine, whereas the business aspects of CH have received less research attention.

UNSTRUCTURED The evidence that quality of life is a positive variable for survival of cancer patients has prompted the interest of the health and pharmaceutical industry in considering that variable as a final clinical outcome. Sustained improvements in cancer care in recent years have resulted in increased numbers of people living with and beyond cancer, with increased attention being placed on improving quality of life for those individuals. Connected Health provides the foundations for the transformation of cancer care into a patient-centric model, focused on providing fully connected, personalized support and therapy for the unique needs of each patient. Connected Health creates an opportunity to overcome barriers to health care support among patients diagnosed with chronic conditions. This paper provides an overview of important areas for the foundations of the creation of a new Connected Health paradigm in cancer care. Here we discuss the capabilities of mobile and wearable technologies; we also discuss pervasive and persuasive strategies and device systems to provide multidisciplinary and inclusive approaches for cancer patients for mental well-being, physical activity promotion, and rehabilitation. Several examples already show that there is enthusiasm in strengthening the possibilities offered by Connected Health in persuasive and pervasive technology in cancer care. Developments harnessing the Internet of Things, personalization, patient-centered design, and artificial intelligence help to monitor and assess the health status of cancer patients. Furthermore, this paper analyses the data infrastructure ecosystem for Connected Health and its semantic interoperability with the Connected Health economy ecosystem and its associated barriers. Interoperability is essential when developing Connected Health solutions that integrate with health systems and electronic health records. Given the exponential business growth of the Connected Health economy, there is an urgent need to develop mHealth exponentially, making it both an attractive and challenging market. In conclusion, there is a need for user-centered and multidisciplinary standards of practice to the design, development, evaluation, and implementation of Connected Health interventions in cancer care to ensure their acceptability, practicality, feasibility, effectiveness, affordability, safety, and equity.

The evidence that quality of life is a positive variable for the survival of cancer patients has prompted the interest of the health and pharmaceutical industry in considering that variable as a final clinical outcome. Sustained improvements in cancer care in recent years have resulted in increased numbers of people living with and beyond cancer, with increased attention being placed on improving quality of life for those individuals. Connected Health provides the foundations for the transformation of cancer care into a patient-centric model, focused on providing fully connected, personalized support and therapy for the unique needs of each patient. Connected Health creates an opportunity to overcome barriers to health care support among patients diagnosed with chronic conditions. This paper provides an overview of important areas for the foundations of the creation of a new Connected Health paradigm in cancer care. Here we discuss the capabilities of mobile and wearable technologies; we also discuss pervasive and persuasive strategies and device systems to provide multidisciplinary and inclusive approaches for cancer patients for mental well-being, physical activity promotion, and rehabilitation. Several examples already show that there is enthusiasm in strengthening the possibilities offered by Connected Health in persuasive and pervasive technology in cancer care. Developments harnessing the Internet of Things, personalization, patient-centered design, and artificial intelligence help to monitor and assess the health status of cancer patients. Furthermore, this paper analyses the data infrastructure ecosystem for Connected Health and its semantic interoperability with the Connected Health economy ecosystem and its associated barriers. Interoperability is essential when developing Connected Health solutions that integrate with health systems and electronic health records. Given the exponential business growth of the Connected Health economy, there is an urgent need to develop mHealth (mobile health) exponentially, making it both an attractive and challenging market. In conclusion, there is a need for user-centered and multidisciplinary standards of practice to the design, development, evaluation, and implementation of Connected Health interventions in cancer care to ensure their acceptability, practicality, feasibility, effectiveness, affordability, safety, and equity. European Cooperation in Science and Technology (COST) ENJECT TD 1405 European Union's Horizon 2020 grant number: 722012 Tatra banka Foundation (2018vs108) Research Agency in Slovakia (ITMS: 26240120038) Fundação para a Ciência e a Tecnologia (FCT) SFRH/BSAB/142983/2018 Fundação para a Ciência e a Tecnologia (FCT) UID/DTP/00617/2019

Objective of the presented study is to introduce telemedicine system that integrates all of the most implemented features that appear in different mobile diabetes applications in addition to blood glucose: tracking of insulin or other medications, communication, diet management, physical activity, weight, blood pressure, personal health record, education, social media, and alerts. Presented telemedicine system employs these features for management of Type 1 and Type 2 diabetes patients. Clinical evaluation after 3 months of intervention showed change of HbA1c in both patient groups was not only statistical significant but it had also a clinical dimension. The HbA1c decreased by 0,5% represents significant change for reduction of diabetic complications. Baseline for HbA1c in 29 T1DM patients: 8.1±1.5% versus 7.5±1.2% (P = 0.008). Baseline for HbA1c in 26 T2DM patients: 7.4±1.2% versus 6.8±1.1% (p=0.001). Results show statistically significant tendency to decrease in T2DM patients in weight 93.2±13.6kg compared to 91.9±14.2kg (P=0.08); and BMI 30.7±4.1 kg/m2 compared to 30.2±4.2 kg/m2 (P=0.08).

Recent developments in society show significant trends in aging population and prevalence of chronic conditions. It is estimated that disruptive demographics related to population of middle-aged and older adults will result in 33% of overall EU population by 2025. Advances in technology created innovative means to support effective management of these challenges through telehealth model for healthcare delivery. In this chapter we introduce decision support in hypertension management with ontology describing the structure of the relevant domain data and analysis of such data using a rule-based system. Telehealth solution provides a ‘complete-loop’ concept for hypertension management with sensor device, mobile, and web-based applications providing means for health status management for both healthcare consumer and healthcare provider.

Background This study examines the development of the connected health (CH) research landscape with a view to providing an overview of the existing CH research. The research field of CH has experienced rapid growth coinciding with increasing pressure on health care systems to become more proactive and patient centered. Objective This study aimed to assess the extent and coverage of the current body of knowledge in CH. In doing so, we sought to identify specific topics that have drawn the attention of CH researchers and to identify research gaps, in particular those offering opportunities for further interdisciplinary research. Methods A systematic mapping study that combined scientific contributions from research in the disciplines of medicine, business, computer science, and engineering was used. Overall, seven classification criteria were used to analyze the papers, including publication source, publication year, research type, empirical type, contribution type, research topic, and the medical condition studied. Results The search resulted in 208 papers that were analyzed by a multidisciplinary group of researchers. The results indicated a slow start for CH research but showed a more recent steady upswing since 2013. The majority of papers proposed health care solutions (77/208, 37.0%) or evaluated CH approaches (49/208, 23.5%). Case studies (59/208, 28.3%) and experiments (55/208, 26.4%) were the most popular forms of scientific validation used. Diabetes, cancer, multiple sclerosis, and heart conditions were among the most prevalent medical conditions studied. Conclusions We conclude that CH research has become an established field of research that has grown over the last five years. The results of this study indicate a focus on technology-driven research with a strong contribution from medicine, whereas the business aspects of CH have received less research attention.

Paper focuses on evaluation of clinical outcomes in management of diabetic patients based on proactive care. Patients were empowered toward self-management through integrated telemedicine system. We used two different patient datasets to evaluate clinical outcomes - dataset A with 95 T1DM (Type 1 diabetes mellitus) and 121 T2DM (Type 2 diabetes) patients and dataset B with 148 T1DM and 218 T2DM patients. The results show significant improvements for both datasets in HbA1c (glycosylated hemoglobin) for T1DM and T2DM patients in 6 month period. Baseline for HbA1c in Dataset A for T1DM patients: 7.55±1.68% versus 7.21±1.59% after six months using telemedicine services (p = 0.003). Baseline for HbA1c for T2DM patients: 6.98±1.55% versus 6.56±1.42% (p = 0). Baseline for HbA1c in Dataset B for T1DM patients: 7.73±1.62% versus 7.38±1.57% after six months using telemedicine services (p = 0). Baseline for HbA1c for T2DM patients: 7.1±1.54% versus 6.77±1.45% (p = 0).

Modern knowledge representation is a very dynamic domain because of continuous research and development. This paper presents Logical Petri Nets (LPNs) and Fuzzy Petri Nets (FPNs) as models for knowledge representation. It is shown that knowledge propagation, described using logical and fuzzy Petri nets, terminates in a unique stable state. Based on this result, the paper introduces an algorithm for knowledge propagation in decision support systems.

Presented telehealth system employs ‘complete loop’ approach in management of hypertension patients with comorbidities consisting of remote patient's data acquisition, data collection, expert review and feedback. Expert review employs ontology and editable rule module for generating alerts based on the values of blood pressure and contextual information related to patient's health status (discomfort, headache, etc.). Feedback is carried out by healthcare provider contacting patients by phone call to manage their medication and lifestyle habits. Based on usability evaluation patients are willing to use telehealth system in long term (for several years) in management of their hypertension condition.

Application of mobile devices in healthcare is a pervasive way how to asses patient health status. Adding just another data source to overwhelmed physician requires technologies for their effective processing. Despite the fact that the problem of extracting clinical information from free-text health reports for computerized applications or for decision support systems is a lot discussed issue, it is still complicated and unresolved question. In general Natural language processing (NLP) systems are implemented to solve the task. However NLP system works only for relatively narrow clinical domains because the format of the language used in the reports is not standardized and the reports vary depending on the domain. We describe methodology suggested for extracting structured data from EHR focusing especially on EHR in Slovak language. Further, we discuss problems concerning a task of extracting required data from free-text health reports. In the conclusion we present test results and possible implementations.

The purpose of this paper is to highlight the best practices in development of telemedicine services for self-management of patients with diabetes. Target group consisted of type 1 patients of age 27–38. These practices are derived from authors experience, patients and physicians involvement, literature and existing solutions review. Developed mHealth telemedicine system is introduced. Results on usability and acceptability of the proposed solution are described from field testing and patients' feedback. Discussion on elements influencing the adoption of telemedicine solutions is introduced.

In this chapter, we consider biometric recognition based on human face. Biometrics became frequently used in automated systems for identification of people (Jain et al., 2004) and huge interest is devoted to the area of biometrics at present (Jain et al., 2008; Shoniregun & Crosier, 2008; Ross et al, 2006). Along with well-known methods such as fingerprint or DNA recognition, face image already opened new possibilities. Face recognition has been put into real life by many companies. It is already implemented in image organizing software (e.g. Google’s Picasa: http://www.deondesigns.ca/blog/picasa-3-5-adds-face-recognition/), web applications (e.g. web photo albums http://picasa.google.com/intl/en_us/features-nametags.html) and even in commercial compact cameras (e.g. Panasonic Lumix). Passports contain face biometric data since 2006 (EU – Passport Specification, 2006). In the area of face recognition, a class represents all images of the same subject (person). The goal is to implement an automated machine supported system that recognizes well the identity of a person in the images that were not used in a training phase (an initialization and training by representative sample of images precede an evaluation phase). Various applications are possible, e.g. automated person identification, recognition of race, gender, emotion, age etc. The area of face recognition is well described at present, e.g. starting by conventional approaches (PCA, LDA) (Turk & Pentland1991; Marcialis & Roli, 2002; Martinez & Kak, 2001), and continuing at present by kernel methods (Wang, et al., 2008; Hotta, 2008; Wang et al., 2004; Yang, 2002; Yang et al., 2005). Advances in face recognition are summarized also in books (Li & Jain, 2005; Delac et al., 2008) and book chapters (Oravec et al., 2008). Our aim is to present complex view to biometric face recognition including methodology, settings of parameters of selected methods (both conventional and kernel methods), detailed recognition results, comparison and discussion of obtained results using large face database. The rest of this chapter is organized as follows: In section 2, we present theoretical background of methods used for face recognition purposes - PCA (Principal Component

We propose and evaluate an approach for network intrusion detection in high dimensional space. This approach is based on an approximate solution to the nearest neighbor problem. Our evaluation is based on the KDD'99 data set, a Yahoo web spam data set and another set used in the NIPS'03 feature selection challenge. The approximate approach shows that good performance in terms of detection rate and false positives rate can be achieved.

We present results for the formalism of extended fuzzy Petri Nets by enriching the enabling and firing rule by introducing the so-called weights and thresholds in order to filter the propagated knowledge. It is shown that knowledge propagation described using such extended fuzzy Petri nets still terminates in a unique stable state. Based on this result, the paper introduces an algorithm for knowledge propagation in medical decision support systems. We also discuss the general properties of knowledge propagation functions terminating in a unique stable state leading to the final recommendation of diagnose or treatment.

Lack of standardization in estimating the quality of diagnostic systems provides many possibilities in developing new methodologies. The paper deals with general method for determination of effectiveness of diagnostic systems in non-invasive diagnostics of thyroid. The result of presented methodology is a single number, which clearly depicts the quality of such a system with no need for further analysis. Together with the general design of such a system an overview of characteristics of a diagnostic system is described.

One of the problems in developing of diagnostic systems is estimation of their quality. In this paper one of the possible solutions is presented. It is based on the statistic appreciation of the quality of knowledge base considering the constant set of input information. The presented method enables representation of the quality of such a system by a single number. It is based on "losses", defined by expert, that describes the seriousness of the incorrect diagnose determination by the diagnostic system.