Why Introduce Machine Learning To Rural Health Care?

Machine learning, the process of teaching a machine to recognize patterns without explicitly being programmed, can provide to medical personnel a powerful tool that can dramatically improve rural patient health

AN AUTOMATIC METHOD FOR CLASSIFYING MEDICAL RESEARCHERS INTO DOMAIN SPECIFIC SUBGROUPS

Objective:This dissertation developed an automatic classification procedure, as an example of a novel tool for an informationist, which extracts information from published abstracts, classifies abstracts into their "fields of study," and then determines the researcher's "field of study" and "level of activity." Method: This dissertation compared a domain expert's method of classification and an automatic classification procedure on a random sample of 101 medical researchers (derived from a potential list of 305 medical researchers) and their associated abstracts. Design: The study design is a retrospective, cross-sectional, inter-rater agreement study, designed to compare two classification methods (i.e., automatic classification procedure and domain expert). The study population consists of University of Pittsburgh, School of Medicine, Department of Medicine (DOM) professionals who (1) have published at least one article listed in PubMed® as first or last author and/or (2) are the primary investigator for at least one grant listed in CRISP.Main outcome measures: Three outcome measures were derived from the domain expert's versus automatic categorization procedure: (1) an abstract's "field of study," (2) a researcher's "field of study" and (3) a researcher's "level of activity and field of study." Results: Kappa showed moderate agreement between automatic and domain expert classification for the abstracts' "field of study" (Kappa = 0.535, n = 504, p < .000). Kappa showed moderate agreement between automatic and domain expert classification of the researcher's "field of study" (Kappa = 0.535, n = 101, p < .000). Kappa showed good agreement between automatic and domain expert classification of the researcher's "level of activity and field of study" (Kappa = 0.634, n = 101, p < .000). Conclusion: The study suggests that an automatic library classification procedure can provide rapid classification of medical research abstracts into their "fields of study." The classification procedure can also process multiple abstracts' "fields of study" and classify their associated medical researchers into their "field of study" and "level of activity and field of study." The classification procedure, used as a tool by an informationist, can be used as the basis for new services

How Unstructured Data from the Data Warehouse Can be Used with Machine Learning and Visualization to Develop Novel Medical Technologies

Machine Learning (ML) can accurately classify and accurately predict disease as well as other medical events. Device Programming, especially smartphone applications, can provide new ways to acquire, transport, store, process, and secure personalized patient data to deliver meaningful results

A first assessment of operator compliance and dolphin behavioural responses during swim-with-dolphin programs for three species of Delphinids in the Azores

The popularity of swim-with wild dolphin programs around the world is fast growing, with the studies required to investigate their impact lagging behind. In the Azores, species targeted include the short-beaked common (Delphinus delphis), the bottlenose (Tursiops truncatus) and the Atlantic spotted dolphin (Stenella frontalis). To evaluate the effects of this activity on local dolphin populations, and thus provide support for management decisions, dolphin response data were collected onboard commercial boats off São Miguel Island between 2013 and 2015. All three species revealed high degree of neutral and avoidance behaviours, and very low approach rates. Tursiops showed higher frequency of neutral responses than Delphinus, while Stenella both avoided and approached more frequently than the other species. When boats intersected the path of dolphin groups, avoidance responses were more likely and the duration of swims was shorter. Swims were also shorter when animals were resting and travelling, and when groups were smaller. The operators generally complied with the legislation, except in respect to the number of swim attempts per dolphin group, which was higher than the legal maximum. Improvement of the current legislation and concurrent reinforcement of controls is essential to avoid detrimental long-term effects of this activity on dolphin populations in the Azores.This research was partially supported by the European Regional Development Fund (ERDF) through the COMPETE – Operational Competitiveness Programme and national funds through FCT – Foundation for Science and Technology, under the project PEst-C/MAR/LA0015/2013, by the Strategic Funding UID/Multi/04423/2013 through national funds provided by FCT – Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020 and by cE3c funding (Ref:UID/BIA/003329/2013). It was also partly supported by CIRN (Centro de Investigação de Recursos Naturais, University of the Azores), and CIIMAR (Interdisciplinary Centre of Marine and Environmental Research, Porto, Portugal). A. Cecchetti was supported by the Regional Fund for Science through the scholarship M.3.1.2/F/036/2011. K.A. Stockin was supported by a Royal Society of New Zealand Te Aparangi Rutherford Discovery Fellowship.info:eu-repo/semantics/publishedVersio

A Framework for Enhanced QoS Support in IEEE 802.11e Networks

IEEE 802.11 based WLANs have became popular, but they can only provide best effort services and so they are poorly suitable for multimedia applications. Recently IEEE 802.11e standard has been proposed to support quality of service. The new standard introduces a so-called Hybrid Coordination Function containing two medium access mechanisms: contention-based channel access and controlled channel access. In this paper we propose a novel framework to better support QoS guarantees for multimedia applications. It comprises QoS Manager, Admission Control, Enhanced Scheduler, Predictor and Feedback System. The scheduler adopted supports real-time applications, variable packet sizes and variable bit rate traffic streams. We show that this framework is suitable to be used by applications requesting Application Level Contracts which will be translated in Resource Level Contracts to the scheduler subsystem. The QoS manager component is able to dynamically manage available resources under different load conditions

Improving the QoS of IEEE 802.11e networks through imprecise computation

IEEE 802.11e HCCA reference scheduler is based on fixed value parameters that do not adapt to traffic changes, thus quality of service (QoS) for multimedia applications is a challenge, especially in the case of variable bit rate (VBR) streams, that requires dynamic resource assignment. This paper is focused on immediate dynamic TXOP HCCA (IDTH) scheduling algorithm and its new evolution immediate dynamic TXOP HCCA plus (IDTH+). Their reclaiming mechanisms, refined by the monitoring of transmission duration, aim at overcoming the limits of fixed preallocation of resources by varying the stations transmission time and avoiding waste of resources. Simulations and theoretical analysis based on the imprecise computation model show that the integration of IDTH and IDTH+ can achieve improved network performance in terms of transmission queues length, mean access delay and packets drop rate, and to efficiently manage bursty traffic. Moreover, the performance improvements of IDTH+ with respect to IDTH are highlighted

Enhancement of QoS support of HCCA schedulers using EDCA function in IEEE 802.11e networks

The IEEE 802.11e standard introduces Quality of Service support for wireless local area networks through two MAC functions: Enhanced Distributed Channel Access (EDCA) and HCF Controlled Channel Access (HCCA). While the former provides prioritized contention-based access to the medium, the latter uses a parameterized contention-free polling scheme. Several studies have proposed enhancements to EDCA or improved scheduling algorithms for HCCA to properly support VBR traffic. However, the cooperation between these functions has only marginally been considered and the solutions vary depending on specific traffic requirements. In this paper we propose a novel approach to address the problem of scheduling VBR traffic streams. Our scheduler, named Overboost, uses HCCA to negotiate a minimum bandwidth and deals with traffic streams that require more bandwidth than the negotiated one by redirecting the excess bandwidth to the EDCA function. An analytical evaluation has been conducted and the results has been corroborated by an extensive set of simulations. They show that the overall scheduler improves the performance with respect to other HCCA schedulers in terms of null rate, throughput, access delay, and queue length

Introducing database communication technologies for TED replication in multi-domain networks

In multi-domain transport networks, exchange of Traffic Engineering information is required to enable effective end-to-end service provisioning and restoration by efficiently utilizing network resources. So far, several solutions have been proposed by the communication community such as the Hierarchical Path Computation Element (H-PCE) architecture. Using the H-PCE architecture a parent PCE is responsible for inter-domain path computation, while a dedicated child PCE performs intra-domain path computation within each domain. However, this approach can introduce scalability concerns especially under dynamic traffic condition such as during restoration because all path computation procedures are coordinated by the parent PCE and may require the exchange of many control messages. This paper proposes a standard communication among database systems located at the child PCEs, to exchange and share YANG-based Traffic Engineering information in multi-domain networks. By exploiting currently available database technologies, scalable and predictable performance is demonstrated for both replication mechanisms among child PCEs and information retrieval from the stored databases. Thus, this proposal enables the sharing of intra-domain information at each cPCE that can be locally used, upon failure, to speed-up the recovery procedure

Why Introduce Machine Learning To Rural Health Care?

Machine learning, the process of teaching a machine to recognize patterns without explicitly being programmed, can provide to medical personnel a powerful tool that can dramatically improve rural patient health