Realizzazione di un sistema di Social Business Intelligence basato sul motore SPSS

Realizzazione di un sistema di Social Business Intelligence basato sul motore SPSS.

Adequacy of venous thromboprophylaxis in acutely ill medical patients (IMPART): multisite comparison of different clinical decision support systems

BACKGROUND: The adequacy of thromboprophylaxis prescriptions in acutely ill hospitalized medical patients needs improvement. OBJECTIVE: To prospectively assess the efficacy of thromboprophylaxis adequacy of various clinical decision support systems (CDSS) with the aim of increasing the use of explicit criteria for thromboprophylaxis prescription in nine Swiss medical services. METHODS: We randomly assigned medical services to a pocket digital assistant program (PDA), pocket cards (PC) and no CDSS (controls). In centers using an electronic chart, an e-alert system (eAlerts) was developed. After 4 months, we compared post-CDSS with baseline thromboprophylaxis adequacy for the various CDSS and control groups. RESULTS: Overall, 1085 patients were included (395 controls, 196 PC, 168 PDA, 326 eAlerts), 651 pre- and 434 post-CDSS implementation: 472 (43.5%) presented a risk of VTE justifying thromboprophylaxis (31.8% pre, 61.1% post) and 556 (51.2%) received thromboprophylaxis (54.2% pre, 46.8% post). The overall adequacy (% patients with adequate prescription) of pre- and post-CDSS implementation was 56.2 and 50.7 for controls (P = 0.29), 67.3 and 45.3 for PC (P = 0.002), 66.0 and 64.9 for PDA (P = 0.99), 50.5 and 56.2 for eAlerts (P = 0.37), respectively, eAlerts limited overprescription (56% pre, 31% post, P = 0.01). CONCLUSION: While pocket cards and handhelds did not improve thromboprophylaxis adequacy, eAlerts had a modest effect, particularly on the reduction of overprescription. This effect only partially contributes to the improvement of patient safety and more work is needed towards institution-tailored tools.

Knowledge Management and Decision Support for Sustainable Land Management

Much research has focused on desertification and land degradation assessments without putting sufficient emphasis on prevention and mitigation, although the concept of sustainable land management (SLM) is increasingly being acknowledged. A variety of SLM measures have already been applied at the local level, but they are rarely adequately recognised, evaluated, shared or used for decision support. WOCAT (World Overview of Technologies and Approaches) has developed an internationally recognised, standardised methodology to document and evaluate SLM technologies and approaches, including spatial distribution, allowing the sharing of SLM knowledge worldwide. The recent methodological integration into a participatory process allows now analysing and using this knowledge for decision support at the local and national level. The use of the WOCAT tools stimulates evaluation (self-evaluation as well as learning from comparing experiences) within SLM initiatives where all too often there is not only insufficient monitoring but also a lack of critical analysis. The comprehensive questionnaires and database system facilitate to document, evaluate and disseminate local experiences of SLM technologies and their implementation approaches. This evaluation process - in a team of experts and together with land users - greatly enhances understanding of the reasons behind successful (or failed) local practices. It has now been integrated into a new methodology for appraising and selecting SLM options. The methodology combines a local collective learning and decision approach with the use of the evaluated global best practices from WOCAT in a concise three step process: i) identifying land degradation and locally applied solutions in a stakeholder learning workshop; ii) assessing local solutions with the standardised WOCAT tool; iii) jointly selecting promising strategies for implementation with the help of a decision support tool. The methodology has been implemented in various countries and study sites around the world mainly within the FAO LADA (Land Degradation Assessment Project) and the EU-funded DESIRE project. Investments in SLM must be carefully assessed and planned on the basis of properly documented experiences and evaluated impacts and benefits: concerted efforts are needed and sufficient resources must be mobilised to tap the wealth of knowledge and learn from SLM successes.

Clinical decision support capabilities of commercially-available clinical information systems.

BACKGROUND: The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. METHODS: The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. RESULTS: Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. CONCLUSIONS: These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

Ontology-driven legal support-system in air transport passenger domain

This paper aims to present a preliminary version of asupport-system in the air transport passenger domain. This system relies upon an underlying on-tological structure representing a normative framework to facilitatethe provision of contextualized relevant legal information.This information includes the pas-senger's rights and itenhances self-litigation and the decision-making process of passengers.Our contribution is based in the attempt of rendering a user-centric-legal informationgroundedon case-scenarios of the most pronounced incidents related to the consumer complaints in the EU.A number ofadvantages with re-spect to the current state-of-the-art services are discussed and a case study illu-strates a possible technological application.

Estudio de patrones de interacción entre los estudiantes y la Plataforma de Tele-Enseñanza en la UPM

Vivimos en una sociedad en la que la información ha adquirido una vital importancia. El uso de Internet y el desarrollo de nuevos sistemas de la información han generado un ferviente interés tanto de empresas como de instituciones en la búsqueda de nuevos patrones que les proporcione la clave del éxito. La Analítica de Negocio reúne un conjunto de herramientas, estrategias y técnicas orientadas a la explotación de la información con el objetivo de crear conocimiento útil dentro de un marco de trabajo y facilitar la optimización de los recursos tanto de empresas como de instituciones. El presente proyecto se enmarca en lo que se conoce como Gestión Educativa. Se aplicará una arquitectura y modelo de trabajo similar a lo que se ha venido haciendo en los últimos años en el entorno empresarial con la Inteligencia de Negocio. Con esta variante, se pretende mejorar la calidad de la enseñanza, agilizar las decisiones dentro de la institución académica, fortalecer las capacidades del cuerpo docente y en definitiva favorecer el aprendizaje del alumnado. Para lograr el objetivo se ha decidido seguir las etapas del Knowledge Discovery in Databases (KDD), una de las metodologías más conocidas dentro de la Inteligencia de Negocio, que describe el procedimiento que va desde la selección de la información y su carga en sistemas de almacenamiento, hasta la aplicación de técnicas de minería de datos para la obtención nuevo conocimiento. Los estudios se realizan a partir de la información de la activad de los usuarios dentro la plataforma de Tele-Enseñanza de la Universidad Politécnica de Madrid (Moodle). Se desarrollan trabajos de extracción y preprocesado de la base de datos en crudo y se aplican técnicas de minería de datos. En la aplicación de técnicas de minería de datos, uno de los factores más importantes a tener en cuenta es el tipo de información que se va a tratar. Por este motivo, se trabaja con la Minería de Datos Educativa, en inglés, Educational Data Mining (EDM) que consiste en la aplicación de técnicas de minería optimizadas para la información que se genera en entornos educativos. Dentro de las posibilidades que ofrece el EDM, se ha decidido centrar los estudios en lo que se conoce como analítica predictiva. El objetivo fundamental es conocer la influencia que tienen las interacciones alumno-plataforma en las calificaciones finales y descubrir nuevas reglas que describan comportamientos que faciliten al profesorado discriminar si un estudiante va a aprobar o suspender la asignatura, de tal forma que se puedan tomar medidas que mejoren su rendimiento. Toda la información tratada en el presente proyecto ha sido previamente anonimizada para evitar cualquier tipo de intromisión que atente contra la privacidad de los elementos participantes en el estudio. ABSTRACT. We live in a society dominated by data. The use of the Internet accompanied by developments in information systems has generated a sustained interest among companies and institutions to discover new patterns to succeed in their business ventures. Business Analytics (BA) combines tools, strategies and techniques focused on exploiting the available information, to optimize resources and create useful insight. The current project is framed under Educational Management. A Business Intelligence (BI) architecture and business models taught up to date will be applied with the aim to accelerate the decision-making in academic institutions, strengthen teacher´s skills and ultimately improve the quality of teaching and learning. The best way to achieve this is to follow the Knowledge Discovery in Databases (KDD), one of the best-known methodologies in B.I. This process describes data preparation, selection, and cleansing through to the application of purely Data Mining Techniques in order to incorporate prior knowledge on data sets and interpret accurate solutions from the observed results. The studies will be performed using the information extracted from the Universidad Politécnica de Madrid Learning Management System (LMS), Moodle. The stored data is based on the user-platform interaction. The raw data will be extracted and pre-processed and afterwards, Data Mining Techniques will be applied. One of the crucial factors in the application of Data Mining Techniques is the kind of information that will be processed. For this reason, a new Data Mining perspective will be taken, called Educational Data Mining (EDM). EDM consists of the application of Data Mining Techniques but optimized for the raw data generated by the educational environment. Within EDM, we have decided to drive our research on what is called Predictive Analysis. The main purpose is to understand the influence of the user-platform interactions in the final grades of students and discover new patterns that explain their behaviours. This could allow teachers to intervene ahead of a student passing or failing, in such a way an action could be taken to improve the student performance. All the information processed has been previously anonymized to avoid the invasion of privacy.

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

The Program Manager's Support System (PMSS) : an executive overview and description of functional modules.

Shipping list no.: 92-370-P.

Designing a support system for aerospace maintenance supply chains

Purpose - To develop a systems strategy for supply chain management in aerospace maintenance, repair and overhaul (MRO). Design/methodology/approach - A standard systems development methodology has been followed to produce a process model (i.e. the AMSCR model); an information model (i.e. business rules) and a computerised information management capability (i.e. automated optimisation). Findings - The proof of concept for this web-based MRO supply chain system has been established through collaboration with a sample of the different types of supply chain members. The proven benefits comprise new potential to minimise the stock holding costs of the whole supply chain whilst also minimising non-flying time of the aircraft that the supply chain supports. Research limitations/implications - The scale of change needed to successfully model and automate the supply chain is vast. This research is a limited-scale experiment intended to show the power of process analysis and automation, coupled with strategic use of management science techniques, to derive tangible business benefit. Practical implications - This type of system is now vital in an industry that has continuously decreasing profit margins; which in turn means pressure to reduce servicing times and increase the mean time between them. Originality/value - Original work has been conducted at several levels: process, information and automation. The proof-of-concept system has been applied to an aircraft MRO supply chain. This is an area of research that has been neglected, and as a result is not well served by current systems solutions. © Emerald Group Publishing Limited.

The Fuzzy-Neuro Classifier for Decision Support

This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem.

Analogous Reasoning and Case-based Reasoning for Intelligent Decision Support Systems

Methods of analogous reasoning and case-based reasoning for intelligent decision support systems are considered. Special attention is drawn to methods based on a structural analogy that take the context into account. This work was supported by RFBR (projects 02-07-90042, 05-07-90232).

La Business Intelligence per il monitoraggio delle vendite: il caso Ducati Motor Holding

“La Business Intelligence per il monitoraggio delle vendite: il caso Ducati Motor Holding”. L’obiettivo di questa tesi è quello di illustrare cos’è la Business Intelligence e di mostrare i cambiamenti verificatisi in Ducati Motor Holding, in seguito alla sua adozione, in termini di realizzazione di report e dashboard per il monitoraggio delle vendite. L’elaborato inizia con una panoramica generale sulla storia e gli utilizzi della Business Intelligence nella quale vengono toccati i principali fondamenti teorici: Data Warehouse, data mining, analisi what-if, rappresentazione multidimensionale dei dati, costruzione del team di BI eccetera. Si proseguirà mediante un focus sui Big Data convogliando l’attenzione sul loro utilizzo e utilità nel settore dell’automotive (inteso nella sua accezione più generica e cioè non solo come mercato delle auto, ma anche delle moto), portando in questo modo ad un naturale collegamento con la realtà Ducati. Si apre così una breve overview sull’azienda descrivendone la storia, la struttura commerciale attraverso la quale vengono gestite le vendite e la gamma dei prodotti. Dal quarto capitolo si entra nel vivo dell’argomento: la Business Intelligence in Ducati. Si inizia descrivendo le fasi che hanno fino ad ora caratterizzato il progetto di Business Analytics (il cui obiettivo è per l'appunto introdurre la BI i azienda) per poi concentrarsi, a livello prima teorico e poi pratico, sul reporting sales e cioè sulla reportistica basata sul monitoraggio delle vendite.

Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.