BINET - Analisi di dati sanitari mediante SNA Validit? degli indicatori statistici ottenuti rispetto a metodologie tradizionali

Social Network technology in the healthcare field aims to establish a non-conventional graph analysis methodology. BINET project (Business Intelligence framework based on Social Network technology) has developed an application framework to test SN methodologies on health data. Scientific validation of the methodology focuses on analyzing therapeutic, time and spatial associations among treatments, e.g. drug prescriptions and diagnosis made during hospitalization, to find correlations between treatments of individuals and patient outcome. In this report, following the testing of the BINET application framework, we compare the validity and accuracy of network statistical indicators with those obtained through traditional methods

The use of social network analysis to study health care provider advice and performance

Introduction: Social network analysis quantifies and visualises communication pathways between actors. This thesis focuses on the application of social network analysis methods to explore healthcare worker professional communication and performance by conducting a systematic review and a network study in Ethiopia. Paper 1: The systematic review analysed what social network methods have been used to study professional communication and performance among healthcare providers. Ten databases were searched from 1990 through April 2016, yielding 5,970 articles. There was marked diversity across all six studies meeting our search criteria in terms of research questions, health sector area, patient outcomes and network analysis methods. The paucity of articles, the complete lack of studies in low and middle-income contexts, the limited number in non-tertiary settings and few longitudinal, experimental designs or network interventions present clear research gaps. Paper 2: A cross-sectional, mixed-methods observational network study captured professional advice networks of 160 healthcare workers in eight primary health care units across four regions of Ethiopia. Data included health care worker advice seeking and giving for the provision of antenatal care, childbirth care, postnatal care and newborn care. Adjacency matrices were uploaded into UCINET 6.0 to calculate network metrics. Networks were visualised using NetDraw. Qualitative interviews of 20 purposively selected subjects followed the collection of quantitative network data to interpret and explain network roles and patterns observed. Expanded field notes were analysed using MaxQDA10. Results: Informal, inter-and intra-cadre advice networks existed. Fellow staff were preferred, but not limited to the primary health care unit. Average network-level metrics: density .26 (SD.11), degree centrality .45 (SD.08), distance 1.94 (SD.26), ties 95.63 (SD 35.46), network size 20.25 (SD 3.65). Conclusion: The systematic review found that network methods are underutilised in this area. The subsequent network study in Ethiopia serves to provide foundational information on healthcare worker professional advice networks

Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption

People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of early illness detection and for monitoring the well-being of those with known illnesses.Comment: 2019 2nd International Conference on Sensors, Signal and Image Processin

A New Zigbee Backoff Approach for Home Healthcare Devices

Most of Healthcare Monitoring System (HMS) used ZigBee, one of the Wireless Sensor Network technologies that offer better mobility, low power consumption and better network scalability. However, ZigBee-based devices face overlapping channel with Wi-Fi devices which cause interference when deployed under the same operating frequency. In this paper, we proposed a new ZigBee algorithm based on Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) to minimize the Wi-Fi interference using experimental approach. Further elaboration highlights the approach, experiment set-up and the analysis metrics for the ZigBee and Wi-Fi coexistence issues. By minimizing the effect of Wi-Fi interference, it will improve the ZigBee transmission reliability which critically required for developing a reliability HMS application. A fragmentation packet management can be considered in the future development to improve packet allocation for large type packet to avoid collision with Wi-Fi packets for better HMS application performance

In-Network Data Reduction Approach Based On Smart Sensing

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real- time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.Scopu

Improved Framework for Blockchain Application Using Lattice Based Key Agreement Protocol

One of the most recent challenges in communicationsystem and network system is the privacy and security ofinformation and communication session. Blockchain is one oftechnologies that use in sensing application in different importantenvironments such as healthcare. In healthcare the patient privacyshould be protected use high security system. Key agreementprotocol based on lattice ensure the authentication and highprotection against different types of attack especiallyimpersonation and man in the middle attack where the latticebased protocol is quantum-withstand protocol. Proposed improvedframework using lattice based key agreement protocol forapplication of block chain, with security analysis of manyliteratures that proposed different protocols has been presentedwith comparative study. The resultant new framework based onlattice overcome the latency limitation of block chain in the oldframework and lowered the computation cost that depend onElliptic curve Diffie-Hellman. Also, it ensures high privacy andprotection of patient’s informatio