Psycho-emotional tools for better treatment adherence and therapeutic outcomes for cancer patients

Personalized medicine should target not only the genetic and clinical aspects of the individual patients but also the different cognitive, psychological, family and social factors involved in various clinical choices. To this direction, in this paper, we present instruments to assess the psycho-emotional status of cancer patients and to evaluate the resilience in their family constructing in such a way an augmented patient profile. Using this profile, 1) information provision can be tailored according to patients characteristics; 2) areas of functioning can be monitored both by the patient and by the clinicians, providing suggestions and alerts; 3) personalized decision aids can be develop to increase patient's participation in the consultation process with their physicians and improve their satisfaction and involvement in the decision-making process. Our preliminary evaluation shows promising results and the potential benefits of the tools

Development of interactive empowerment services in support of personalised medicine

In an epoch where shared decision making is gaining importance, a patient\u2019s commitment to and knowledge about his/her health condition is becoming more and more relevant. Health literacy is one of the most important factors in enhancing the involvement of patients in their care. Nevertheless, other factors can impair patient processing and understanding of health information: psychological aspects and cognitive style may affect the way patients approach, select, and retain information. This paper describes the development and validation of a short and easy to fill-out questionnaire that measures and collects psycho-cognitive information about patients, named ALGA-C. ALGA-C is a multilingual, multidevice instrument, and its validation was carried out in healthy people and breast cancer patients. In addition to the aforementioned questionnaire, a patient profiling mechanism has also been developed. The ALGA-C Profiler enables physicians to rapidly inspect each patient\u2019s individual cognitive profile and see at a glance the areas of concern. With this tool, doctors can modulate the language, vocabulary, and content of subsequent discussions with the patient, thus enabling easier understanding by the patient. This, in turn, helps the patient formulate questions and participate on an equal footing in the decision-making processes. Finally, a preview is given on the techniques under consideration for exploiting the constructed patient profile by a personal health record (PHR). Predefined rules will use a patient\u2019s profile to personalise the contents of the information presented and to customise ways in which users complete their tasks in a PHR system. This optimises information delivery to patients and makes it easier for the patient to decide what is of interest to him/her at the moment

Computational horizons in cancer (CHIC) : developing meta- and hyper-multiscale models and repositories for in Silico Oncology - a brief technical outline of the project

This paper briefly outlines the aim, the objectives, the architecture and the main building blocks of the ongoing large scale integrating transatlantic research project CHIC (http://chic-vph.eu/)

Reviewing the integration of patient data: how systems are evolving in practice to meet patient needs

<p>Abstract</p> <p>Background</p> <p>The integration of Information Systems (IS) is essential to support shared care and to provide consistent care to individuals – patient-centred care. This paper identifies, appraises and summarises studies examining different approaches to integrate patient data from heterogeneous IS.</p> <p>Methods</p> <p>The literature was systematically reviewed between 1995–2005 to identify articles mentioning patient records, computers and data integration or sharing.</p> <p>Results</p> <p>Of 3124 articles, 84 were included describing 56 distinct projects. Most of the projects were on a regional scale. Integration was most commonly accomplished by messaging with pre-defined templates and middleware solutions. HL7 was the most widely used messaging standard. Direct database access and web services were the most common communication methods. The user interface for most systems was a Web browser. Regarding the type of medical data shared, 77% of projects integrated diagnosis and problems, 67% medical images and 65% lab results. More recently significantly more IS are extending to primary care and integrating referral letters.</p> <p>Conclusion</p> <p>It is clear that Information Systems are evolving to meet people's needs by implementing regional networks, allowing patient access and integration of ever more items of patient data. Many distinct technological solutions coexist to integrate patient data, using differing standards and data architectures which may difficult further interoperability.</p

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

Review on Psychological Stress Detection Using Biosignals

This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the body and nervous system to confront the imminent threat and ensure effective adaptation. Biosignals that can be measured reliably in relation to such stressors include physiological (EEG, ECG, EDA, EMG) and physical measures (respiratory rate, speech, skin temperature, pupil size, eye activity). A fundamental objective in this area of psychophysiological research is to establish reliable biosignal indices that reveal the underlying physiological mechanisms of the stress response. Motivated by the lack of comprehensive guidelines on the relationship between the multitude of biosignal features used in the literature and their corresponding behaviour during stress, in this paper, the impact of stress to multiple bodily responses is surveyed. Emphasis is put on the efficiency, robustness and consistency of biosignal data features across the current state of knowledge in stress detection. It is also explored multimodal biosignal analysis and modelling methods for deriving accurate stress correlates. This paper aims to provide a comprehensive review on biosignal patterns caused during stress conditions and reliable practical guidelines towards more efficient detection of stress. © 2010-2012 IEEE