A Framework for Privacy-preserving Classification of Next-generation PHR data.

Personal Health Records (PHRs), integrated with data from various sources, such as social care data, Electronic Health Record data and genetic information, are envisaged as having a pivotal role in transforming healthcare. These data, lumped under the term 'big data', are usually complex, noisy, heterogeneous, longitudinal and voluminous thus prohibiting their meaningful use by clinicians. Deriving value from these data requires the utilization of innovative data analysis techniques, which, however, may be hindered due to potential security and privacy breaches that may arise from improper release of personal health information. This paper presents a HIPAA-compliant machine learning framework that enables privacy-preserving classification of next-generation PHR data. The predictive models acquired can act as supporting tools to clinical practice by enabling more effective prevention, diagnosis and treatment of new incidents. The proposed framework has a huge potential for complementing medical staff expertise as it outperforms the manual inspection of PHR data while protecting patient privacy.

A conceptual security framework for personal health records (PHRs).

Electronic personal health record (PHR) is a citizen-centric information tool that allows citizens to control their personal information. However, an ideal PHR should also allow citizens to connect with their formal and informal caregivers (e.g. a family member, a caregiver) and together manage citizen health and social information. This introduces specific challenges in terms of security since multiple parties make entries and require access to PHR data. Since citizens are typically non-security and non-domain experts is considered impossible to control all this information. To this end, this paper presents a conceptual security framework for the employment of an attribute-based PHR access control policy that is continually updated according to providers' local security policies and individual professionals and citizen sharing preferences.

Android-based access to holistic emergency care record.

This paper is concerned with the development of an Emergency Medical Services (EMS) system which interfaces with a Holistic Emergency Care Record (HECR) that aims at managing emergency care holistically by supporting EMS processes and is accessible by Android-enabled mobile devices.

Video segmentation of moving humans for assistive environments.

In this paper, we present two recently proposed efficient methods for human segmentation from video in indoor environments: the illumination sensitive background method and the self-organizing background subtraction (SOBS) method. Both methods maintain multiple background models. The SOBS method has been modified in this work for gray-scale frames, in order to decrease processing times. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The paper presents the algorithmic implementation and modifications details, while results are also presented for a small number of video sequences.

Elucidating the identity of resistance mechanisms to prednisolone exposure in acute lymphoblastic leukemia cells through transcriptomic analysis: A computational approach.

BACKGROUND: It has been shown previously that glucocorticoids exert a dual mechanism of action, entailing cytotoxic, mitogenic as well as cell proliferative and anti-apoptotic responses, in a dose-dependent manner on CCRF-CEM cells at 72 h. Early gene expression response implies a dose-dependent dual mechanism of action of prednisolone too, something reflected on cell state upon 72 h of treatment. METHODS: In this work, a generic, computational microarray data analysis framework is proposed, in order to examine the hypothesis, whether CCRF-CEM cells exhibit an intrinsic or acquired mechanism of resistance and investigate the molecular imprint of this, upon prednisolone treatment. The experimental design enables the examination of both the dose (0 nM, 10 nM, 22 uM, 700 uM) effect of glucocorticoid exposure and the dynamics (early and late, namely 4 h, 72 h) of the molecular response of the cells at the transcriptomic layer. RESULTS: In this work, we demonstrated that CCRF-CEM cells may attain a mixed mechanism of response to glucocorticoids, however, with a clear preference towards an intrinsic mechanism of resistance. Specifically, at 4 h, prednisolone appeared to down-regulate apoptotic genes. Also, low and high prednisolone concentrations up-regulates genes related to metabolism and signal-transduction in both time points, thus favoring cell proliferative actions. In addition, regulation of NF-κB-related genes implies an inherent mechanism of resistance through the established link of NF-κB inflammatory role and GC-induced resistance. The analysis framework applied here highlights prednisolone-activated regulatory mechanisms through identification of early responding sets of genes. On the other hand, study of the prolonged exposure to glucocorticoids (72 h exposure) highlights the effect of homeostatic feedback mechanisms of the treated cells. CONCLUSIONS: Overall, it appears that CCRF-CEM cells in this study exhibit a diversified, combined pattern of intrinsic and acquired resistance to prednisolone, with a tendency towards inherent resistant characteristics, through activation of different molecular courses of action.

A distributed environment for the integration of multiple high-performance decision support systems into clinical workflow.

Current studies conclude that clinical decision support systems can help reduce serious medical errors. The importance of Causal Probabilistic Networks (CPNs) for constructing such systems is already well-known. However, the computational complexity of probabilistic inference, which results in unacceptably high response times, can hinder acceptance and integration into clinician workflow. This paper investigates the optimization and parallelization potential of complex CPN-based medical decision support systems and evaluates the results of implementing a parallel, high performance version of an existing decision support system concerning proper antibiotic treatment therapy. Furthermore, it discusses distributed computing techniques for making multiple high performance decision support systems available at the time and location of decision making, by exploiting computing resources residing inside, as well as outside the hospital walls optimally.

Communication protocol requirements of patient personal area networks for telemonitoring.

Continuous, remote monitoring of patients' healthcare condition, as this can be reflected by crucial vital signs supervision, is a key demand in modern healthcare provision. Towards the accomplishment of this requirement, the traditional solution of cabled sensors appears to have clear limitations and this is one of the ultimate causes of the proliferation of wireless medical sensor networks during last years. The modern trend towards this direction is the formulation of patient Personal Area Networks (pPANs), consisting of a wireless infrastructure of medical sensors, attached to patient's body, which lays the path for incessant telemonitoring of the person in mind, without discomforting them. The nature of data that these networks are set to handle, as well as the particular demands that patient telemonitoring services raise, necessitate for a thorough analysis of the design requirements of pPAN communication protocols, in order to outflank possible disadvantages appearing in protocols for different types of wireless sensor networks, without putting aside simplicity and feasibility factors. In the context of this paper, we intend to outline the most important requirements and design issues of a pPAN communication protocol, having as compass the main attributes of the most commonly used medical sensors and the typical functionality of these networks.

Cost-effective health services for interactive continuous monitoring of vital signs parameters--the e-Vital concept.

The objective of the e-Vital project is the validation of the market concerning the provision of a novel remote telemedicine service aimed at large sensitive parts of the European population, the "at-risk" citizens, who are usually patients with a stable medical condition that allow a near normal life but may suddenly deteriorate and put life at risk. This service will increase their quality of life and their feeling of safety concerning their health. The e-Vital project focuses on the implementation and exploitation of a modular and ambulatory secure telemedicine platform, which is using easily wearable vital signs monitoring devices, causing minimal discomfort to patients, and which transfer in real time and on-line critical vital parameters to doctors and/or medical experts/consultants, regardless of their location, while getting feedback to increase their feeling of comfort or in case of alarm. The interactive continuous monitoring promises cost effective health services, more active involvement of patients in their own care, and a new sense of realism in making a diagnosis.