User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Keberkesanan modul infusi kemahiran berfikir aras tinggi pembelajaran luar bilik darjah (iKBAT-PLBD) bagi bidang pembelajaran sukatan dan geometri

Kemahiran berfikir aras tinggi (KBAT) merupakan satu kemahiran berfikir yang sangat diperlukan dalam mendepani cabaran kehidupan masa kini terutama dalam bidang matematik. Oleh itu, kajian ini dijalankan untuk mengkaji sama ada KBAT matematik pelajar dapat ditingkatkan dengan menggunakan modul infusi Kemahiran Berfikir Aras Tinggi - Pembelajaran Luar Bilik Darjah (iKBAT–PLBD) atau tidak? Justeru itu, satu kerangka perancangan telah dibuat terhadap empat kemahiran tertinggi dalam Taksonomi Bloom semakan semula yang juga merupakan konstruk utama dalam KBAT. Konstruk KBAT tersebut ialah konstruk menganlisis, mengaplikasi menilai dan mencipta. Sampel kajian ini melibatkan 120 pelajar tingkatan 1 di empat buah sekolah yang berbeza di negeri Johor. Dalam menjalankan kajian kuasi eksperimental ini, data dikumpul melalui kajian keputusan ujian pra dan ujian pos sebelum dan selepas menggunakan modul bagi kumpulan rawatan. Manakala pendekatan PdP tradisional pula digunakan bagi kumpulan kawalan. Hasil daripada analisis data menunjukkan bahawa aktiviti pembelajaran dan pemudahcaraan (PdPc) yang bertunjangkan modul iKBAT–PLBD telah dapat meningkatkan penguasaan matematik pelajar dalam kempat-empat tahap KBAT serta bagi keseluruhan tahap. Dapatan kajian ini menunjukkan terdapat perbezaan yang signifikasi antara kumpulan kawalan dan kumpulan rawatan terhadap peningkatan KBAT pelajar dalam matematik dengan menggunakan pendekatan iKBAT–PLBD bagi tahap mengaplikasi, menganalisis, menilai, mencipta juga secara keseluruhan. Kesimpulannya, kajian ini dapat memberi manfaat kepada semua pihak termasuk pihak Kementerian Pendidikan Malaysia (KPM), pihak pentadbiran sekolah, ibubapa, guru matematik malah bagi pelajar itu dari segi pengubalan dasar yang berkaitan, pengaplikasian dan sebagai satu bukti keberkesanan dalam proses pemerkasaan KBAT matematik di Malaysia

Case-based reasoning combined with statistics for diagnostics and prognosis

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes