Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Dimensions of Timescales in Neuromorphic Computing Systems

This article is a public deliverable of the EU project "Memory technologies with multi-scale time constants for neuromorphic architectures" (MeMScales, https://memscales.eu, Call ICT-06-2019 Unconventional Nanoelectronics, project number 871371). This arXiv version is a verbatim copy of the deliverable report, with administrative information stripped. It collects a wide and varied assortment of phenomena, models, research themes and algorithmic techniques that are connected with timescale phenomena in the fields of computational neuroscience, mathematics, machine learning and computer science, with a bias toward aspects that are relevant for neuromorphic engineering. It turns out that this theme is very rich indeed and spreads out in many directions which defy a unified treatment. We collected several dozens of sub-themes, each of which has been investigated in specialized settings (in the neurosciences, mathematics, computer science and machine learning) and has been documented in its own body of literature. The more we dived into this diversity, the more it became clear that our first effort to compose a survey must remain sketchy and partial. We conclude with a list of insights distilled from this survey which give general guidelines for the design of future neuromorphic systems

Filamentary Threshold Switching In Niobium Oxides

Two-terminal metal/oxide/metal (MOM) structures exhibit characteristic resistance changes, including non-volatile memory and volatile threshold switching responses when subjected to electrical stress (i.e., voltage or current stimuli), which are of interest as active elements in non-volatile memory arrays and neuromorphic computing. Recently, the threshold switching response in MOM devices based on vanadium oxides and niobium oxides have attracted particular attention due to their simple structure and reliability. Interestingly, specific phases of these oxides (e.g., VO2, NbO2 etc.) exhibit a metal-insulator transition (MIT) which causes dramatic changes in their intrinsic properties, including electrical and thermal conductivities, and often arguably reported as the dominant cause of the observed threshold switching response. While this response has been extensively studied for VO2, but the low transition temperature (~ 340K) limits their use only to low temperature microelectronics applications. In contrast, NbO2 has a much higher transition temperature ~ 1070 K, and NbO2 and other NbOx phases have drawn recent attention due to their reliable threshold switching characteristics. The resistance changes in MOM structures are often initiated by a one-step electroforming process that forms a filamentary conduction path. Knowledge about the structure, composition and spatial distribution of these filaments is essential for a full understanding of filamentary resistive/threshold-switching and for effective modelling and optimisation of associated devices. Additionally, NbOx-based devices exhibit a wide range of resistive and threshold switching responses that critically depend on operating condition, composition and device geometry. Thus, a proper understanding of these factors is important for achieving reliable switching with desired characteristics. This thesis focuses on understanding the electroforming process and subsequent threshold switching responses in NbOx by employing different techniques, including electrical testing, and thermo-reflectance imaging. At first, a simple means of detecting and spatially mapping conductive filaments in metal/oxide/metal cross-point devices is introduced and the utility of this technique is demonstrated to identify distinct modes of electroforming in low- and high-conductivity NbOx films. After that, the role of metal/oxide interface reactions on the post-forming characteristics of reactive-metal/Nb2O5/Pt devices is demonstrated. Specifically, devices are shown to exhibit stable threshold switching under negative bias but the response under positive bias depends on the choice of metal. Then, the threshold-switching and current-controlled negative differential resistance (NDR) characteristics of cross-point devices fabricated from undoped Nb2O5 and Ti-doped Nb2O5 are compared. In particular it is shown that doping offers an effective means of engineering the device response. Based on temperature dependent current-voltage characteristics and lumped-element modelling, these effects are attributed to doping-induced reductions in the device resistance and its rate of change with temperature. Finally, the physical origin of the discontinuous 'snapback' NDR is investigated. Specifically, it is shown that the snapback response is a direct consequence of current localisation and redistribution within the oxide film. Furthermore, it is demonstrated that material and device dependencies are consistent with predictions of a two-zone parallel memristor model of NDR which is based on a non-uniform current distribution after electroforming. These results advance the current understanding of threshold switching response in amorphous NbOx films, and provide a strong basis for engineering devices with specific NDR characteristics. Significantly, these results also resolve a long-standing controversy about the origin of the snapback response which has been a subject of considerable debate

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Exploring emergence in interconnected ferromagnetic nanoring arrays

Emergent interactions in periodic, artificial ferromagnetic nanostructures is well explored for magnetic systems such as artificial spin ices (ASI). This work presents a novel approach of an interconnected array of ferromagnetic nanorings to harness emergence in a dynamic system for functionality. Magnetic nanorings have two preferred configurations of magnetisation – ‘vortex’ that contains no domain walls (DWs) and ‘onion’ state with two DWs. In-plane applied rotating fields move DWs around a ring. The junction between interconnected rings presents a pinning potential that must be overcome to continue DW motion. In an ensemble, such as an array of interconnected rings, a sufficiently high field gives unimpeded DW motion. Under a sufficiently low field, no DWs de-pin. Both conserve DW population. Between these limits, de-pinning is probabilistic and field dependent. When one DW in an ‘onion’ state is pinned and the other de-pins, annihilation of DWs will occur and rings convert from ‘onion’ to ‘vortex’. Micromagnetic modelling also shows a DW de-pinning from a junction adjacent to a ‘vortex’ ring repopulates it with DWs. Analytical modelling of DW population revealed an equilibrium that varies non- monotonically with de-pinning probability and varies with array size and geometry. Polarised neutron reflectometry (PNR) and MOKE magnetometry measured arrays of permalloy nanorings. Magnetisation as a function of applied rotating field strength confirmed a non-monotonic response. Magnetic force microscopy (MFM) and photoemission electron microscopy (PEEM) allowed direct observation of DW configurations, revealing: highly ordered arrangements of ‘onion’ states at saturation; minor changes in DW population with low and high strength rotating fields; DW loss and breakdown in long-range order with intermediate fields. Imaging showed junctions produce behaviour analogous to emergent vertex configurations in ASIs. Interconnected nanoring arrays show good candidacy for novel computing architectures, such as reservoir computing, given their dynamic tuneability, non-linear response to an external stimulus, scalability, fading memory and repeatability

Exploring the potential of brain-inspired computing

The gap between brains and computers regarding both their cognitive capability and power efficiency is remarkably huge. Brains process information massively in parallel and its constituents are intrinsically self-organizing, while in digital computers the execution of instructions is deterministic and rather serial. The recent progress in the development of dedicated hardware systems implementing physical models of neurons and synapses enables to efficiently emulate spiking neural networks. In this work, we verify the design and explore the potential for brain-inspired computing of such an analog neuromorphic system, called Spikey. We demonstrate the versatility of this highly configurable substrate by the implementation of a rich repertoire of network models, including models for signal propagation and enhancement, general purpose classifiers, cortical models and decorrelating feedback systems. Network emulations on Spikey are highly accelerated and consume less than 1 nJ per synaptic transmission. The Spikey system, hence, outperforms modern desktop computers in terms of fast and efficient network simulations closing the gap to brains. During this thesis the stability, performance and user-friendliness of the Spikey system was improved integrating it into the neuroscientific tool chain and making it available for the community. The implementation of networks suitable to solve everyday tasks, like object or speech recognition, qualifies this technology to be an alternative to conventional computers. Considering the compactness, computational capability and power efficiency, neuromorphic systems may qualify as a valuable complement to classical computation

In situ investigation of the redox behaviour of strained La0.5Sr0.5Mn0.5Co0.5O3-¿ thin films

Structural changes in transition metal oxides are often considered to be synonymous with changes in the oxygen defect structure of a material. Understanding, and tuning, structural and chemical changes in perovskite oxides is critical for the optimisation of oxygen evolution and reduction reactions, and studying these processes in situ is essential for improving the catalytic performance of perovskites. La0.5Sr0.5Mn0.5Co0.5O3-d (LSMC) has been studied as a model system as it is one of the fewreported perovskite oxides that is stable with d > 0.5, accommodating a fully oxidised phase (d = 0) and reduced phase (d = 0.62) with a reversible, topotactic transition between these two phases in bulk form. In this work, thin films of LSMC were deposited using pulsed laser deposition on single crystal substrates resulting in compressive strain on LaAlO3 (LAO), and tensile strain on (LaAlO3)0.3(Sr2AlTaO6)0.7 (LSAT) and SrTiO3 (STO). Complementary X-ray diffraction (XRD), for determining structural changes, and X-ray absorption near edge spectroscopy (XANES), to determine oxygen stoichiometry, were used to investigate the effects of mechanical strain. The LSMC unit cell volumewas found to be linearly dependant on the strain, however the same relationshipwas not observed for the oxygen stoichiometry. Both the compressively strained LSMC/LAOand LSMC/LSAT were oxidised, while the LSMC/STO was more reduced due to a change in Mn valence. In situ XRD, conducted at elevated temperatures (400 to 750C) with varying oxygen partial pressures (pO2 = 1x10^1 to 2.2x10^5 ppm), demonstrate that the unit cell volume of LSMC responds to changes in oxygen partial pressure. Films grown on LAO show a five times greater cell parameter change between oxidising and reducing conditions compared to those grown on STO, with changes of approximately 3x10^−3 Å and 6.5x10^−4 Å at 700C, respectively. In situ XANES, at temperatures up to 500C under reducing conditions (pO2 < 1x10^−5 ppm), provided evidence for a greater change in the Mn oxidation state. Further, the tensile strain of the LSMC/STO samples was shown to stabilise a LSMC thin film with a lower oxidation stoichiometry, indicating that strain is a successful method for tuning the defect structure under operating conditions. Electrical characterisation was performed by applying bias through LSMC/Nb:STO thin films to change the effective oxygen stoichiometry. These results showed promising resistive switching behaviour and initial in situ measurements with isotopically labelled oxygen provide evidence for an interface based switching mechanism. These results deepen the understanding of methods to tune the defect structure of perovskites and can be used to guide the optimisation of perovskite properties for electrochemical devices including energy storage and memristors.Open Acces

Microscopy Conference 2017 (MC 2017) - Proceedings

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2017", die vom 21. bis 25.08.2017, in Lausanne stattfand