Aida Kamišalić

On reliability indices of communication networks

The aim of this paper is to study the global reliability of communication networks. We assume that, in a communication network, the weights of the edges quantify the volume or the quality of the information transmitted by the nodes. In such a case, the strength of a path (resp. walk), called the reliability of the path (resp. walk) can be calculated as the product of the weights of the edges belonging to the paths (resp. walks). We introduce three indices to compute the reliability of a digraph (resp. graph). The first one is a version of Wiener index where we consider only the most reliable path between each pair of nodes. The second notion of reliability index considers reliability of all walks between each pair of nodes instead of taking into account only the most reliable path. The last one is a generalization of the functional centralization to the case of weighted networks. In this case, the notion of reliability index considers, for each node, the reliability of all closed walks starting and ending in the node. In addition, we propose a method for computing the introduced indices. Application of some of the proposed indices to trust-weighted social networks is also discussed.

Temporal Constraints Approximation from Data about Medical Procedures

Proposing a treatment to patients is one of the physicians most common tasks. There are different elements that influence the decision of a physician to propose an appropriate treatment. Formal intervention plans (FIPs) are formal structures representing health care procedures to assist patients suffering from particular ailments or diseases. The introduction of temporal constraints in FIPs is a difficult task that physicians are not used to. This difficulty can be overcome with mechanisms to generate temporal constraints directly from the existing data on patient treatments. We have chosen the SDA formalism to represent FIPs. Here, our objective is to approximate time constraints from patient state transition sequences and as a generalization of the times assigned to each transition (or patient evolution). This approximation is used to construct the time dimension of FIPs.

Internationalization as a part of the database development

Nowadays development and research projects increasingly extend over boundaries set by a single company, research organisation or country. To compete successfully in this global information technology market, the internationalization of software, data and Web contents for disparate users is essential. Software internationalisation is the process of developing software products that are not dependent on specific local or cultural attributes or practise. It is becoming an increasingly important topic while software, data and Web contents gets integrated into the fabric of society and cultures worldwide. Things like date, time, currency symbols, and delimiters are handled seamlessly. More open are topics like quality assurance, standards and data modelling. Nevertheless to ensure the progress and up to date solutions, integration of internationalization contents in development of database are expected and have to be supported. Education of internationalization and localization-aware computer science professionals is so playing the key role. Experts have to cope with local cultural conventions that influence applications solutions and data modelling.

EduCTX: A Blockchain-Based Higher Education Credit Platform

Blockchain technology enables the creation of a decentralized environment, where transactions and data are not under the control of any third party organization. Any transaction ever completed is recorded in a public ledger in a verifiable and permanent way. Based on the blockchain technology, we propose a global higher education credit platform, named EduCTX. This platform is based on the concept of the European Credit Transfer and Accumulation System (ECTS). It constitutes a globally trusted, decentralized higher education credit, and grading system that can offer a globally unified viewpoint for students and higher education institutions (HEIs), as well as for other potential stakeholders, such as companies, institutions, and organizations. As a proof of concept, we present a prototype implementation of the environment, based on the open-source Ark Blockchain Platform. Based on a globally distributed peer-to-peer network, EduCTX will process, manage, and control ECTX tokens, which represent credits that students gain for completed courses, such as ECTS. HEIs are the peers of the blockchain network. The platform is a first step toward a more transparent and technologically advanced form of higher education systems. The EduCTX platform represents the basis of the EduCTX initiative, which anticipates that various HEIs would join forces in order to create a globally efficient, simplified, and ubiquitous environment in order to avoid language and administrative barriers. Therefore, we invite and encourage HEIs to join the EduCTX initiative and the EduCTX blockchain network.

Generating macro-temporality in timed transition diagrams

Decision support systems in medicine are designed to aid healthcare professionals on making clinical decisions. Clinical Algorithms derived from Clinical Practice Guidelines (CPGs) make explicit the knowledge necessary to assist physicians in order to make appropriate decisions. Decision support systems for healthcare procedures are supposed to answer questions about what to do and with what time restrictions. Unfortunately, so far we are not able to answer the second question, as clinical algorithms do not contain temporal constraints. Here, our objective is to produce explicit knowledge on temporal restrictions for healthcare procedures. This is reached by generating temporal models from hospital databases. First, we have identified macro-temporality as a constraint on the time required to evolve one step in a clinical algorithm. We have decided to use Timed Transition Diagrams (TTDs) as a structure to represent clinical algorithms, extended with macro-temporality constraints. Then we have identified three different data levels in hospital databases and we have proposed an algorithm to generate macro-temporality in TTDs for each data level.

Temporal Knowledge Generation for Medical Procedures

Decision support systems (DSSs) in medicine are designed to aid medical professionals on making clinical decisions about prevention, diagnosis and corresponding treatment. When DSSs are applied to medical procedures, two sorts of predictions are possible: procedural (i.e. indications on what to do), and temporal (i.e. indications on what are the time restrictions). Clinical Practice Guidelines (CPGs) are statements that assist physicians making appropriate medical decisions during patient encounters. They are a set of assertions used to manage patients with a particular disease to improve quality of care, decrease unjustified practice variations and save costs. Clinical algorithms (CAs) obtained from CPGs are introduced to make the procedural knowledge explicit and formal. It is important to enable the latest clinical knowledge to be accessible and usable at the point of care, and therefore make significant contributions to safety and quality in medicine. Medical knowledge is used to assist patients suffering from one or several diseases. CAs could be explicitly given, or obtained with a knowledge management mechanisms. Among these mechanisms, there are some that aim at generating CAs from existing patients’ data for a particular disease. However, either explicitly given or generated CAs are atemporal, which means that there is no an explicit time labelling of the elements in the CA. Time plays a major role in medicine and therefore also in medical information systems. It is an important concept of the real world, which needs to be managed in different ways (events occur at some time points, facts hold during time periods, temporal relationships exist between facts and events) (Combi et al., 2010). If we want to overcome the gap of atemporal CAs it is necessary to define a time dimension and make also temporal knowledge (the indications on what are the time restrictions) explicit and formal. It has been proved that obtaining explicit temporal knowledge from physicians is often a difficult and time-consuming task regardless of the knowledge engineeringmechanisms or tools employed to simplify the process. As data saved in hospital databases are primarily time dependent, they can be used to obtain temporal constraints to define the time dimension of CAs. We have propose generation of temporal constraints considering patients’ data of a particular disease for atemporal CAs. We have defined two types of temporal constraints: macro-temporality and micro-temporality. Macro-temporality is defined as a constraint [tmin, tmax] on the time required to cross a particular edge of a CA, where tmin and tmax are the lower and the upper Temporal Knowledge Generation for Medical Procedures

Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review

Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.

Formalization and acquisition of temporal knowledge for decision support in medical processes

BACKGROUNDnIn medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge.nnnOBJECTIVEnThe objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations.nnnMETHODSnIn order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested.nnnRESULTSnA full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1-7 weeks, whereas CPGs recommend 2-4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1-6 weeks, and 2-5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3-6 month range.nnnCONCLUSIONSnExperience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).

Conceptual Model — Meeting of Cultures

Electronic availability of data and their usage in various domains have upgraded the importance of internationalization. In general, we could say that internationalization has a syntactic and a semantic component. The syntactic component is relatively easy reached, mostly is supported by tools (software translated to national languages), while the semantic component requires further research in the connection with the cultural dialog. Special attention needs to be paid to data upon which critical decisions are met. In the paper we will focus on internationalization in the connection with conceptual modeling including needs for intercultural dialog integrated into the process of conceptual modeling.

Data Policy for Intelligent Systems

Use of data in various areas and their electronic availability has put importance of data to higher level. In many cases data come from different sources, which are distributed across enterprise and are at different quality levels. Special attention need to be paid to data upon which critical decisions are met especially in/for intelligent systems. In the present paper we will focus on data policy for increasing the quality of data used and/or acquired in intelligent systems