Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Organisational structure, communication and group ethics

This paper investigates experimentally how organisational decision processes affect the moral motivations of actors inside a firm that must forego profits to reduce harming a third party. In a "vertical" treatment, one insider unilaterally sets the harm-reduction strategy; the other can only accept or quit. In a "horizontal" treatment, the insiders decide by consensus. Our 2-by-2 design also controls for communication effects. In our data, communication makes vertical firms more ethical; voice appears to mitigate "responsibility-alleviation" in that subordinates with voice feel responsible for what their firms do. Vertical firms are then more ethical than the horizontal firms for which our bargaining data reveal a dynamic form of responsibility-alleviation and our chat data indicate a strong "insider-outsider" effect.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems

Supply chain management: An opportunity for metaheuristics

In today s highly competitive and global marketplace the pressure onorganizations to find new ways to create and deliver value to customersgrows ever stronger. In the last two decades, logistics and supply chainhas moved to the center stage. There has been a growing recognition thatit is through an effective management of the logistics function and thesupply chain that the goal of cost reduction and service enhancement canbe achieved. The key to success in Supply Chain Management (SCM) requireheavy emphasis on integration of activities, cooperation, coordination andinformation sharing throughout the entire supply chain, from suppliers tocustomers. To be able to respond to the challenge of integration there isthe need of sophisticated decision support systems based on powerfulmathematical models and solution techniques, together with the advancesin information and communication technologies. The industry and the academiahave become increasingly interested in SCM to be able to respond to theproblems and issues posed by the changes in the logistics and supply chain.We present a brief discussion on the important issues in SCM. We then arguethat metaheuristics can play an important role in solving complex supplychain related problems derived by the importance of designing and managingthe entire supply chain as a single entity. We will focus specially on theIterated Local Search, Tabu Search and Scatter Search as the ones, but notlimited to, with great potential to be used on solving the SCM relatedproblems. We will present briefly some successful applications.

Low-cost microprocessed instrument for evaluating soil temperature profile

This paper describes a low-cost microprocessed instrument for in situ evaluating soil temperature profile ranging from -20.0°C to 99.9°C, and recording soil temperature data at eight depths from 2 to 128 cm. Of great importance in agriculture, soil temperature affects plant growth directly, and nutrient uptake as well as indirectly in soil water and gas flow, soil structure and nutrient availability. The developed instrument has potential applications in the soil science, when temperature monitoring is required. Results show that the instrument with its individual sensors guarantees ±0.25°C accuracy and 0.1°C resolution, making possible localized management changes within decision support systems. The instrument, based on complementary metal oxide semiconductor devices as well as thermocouples, operates in either automatic or non-automatic mode.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Randomized trial of a computerized coronary heart disease risk assessment tool in HIV-infected patients receiving combination antiretroviral therapy.

BACKGROUND: Exposure to combination antiretroviral therapy (cART) can lead to important metabolic changes and increased risk of coronary heart disease (CHD). Computerized clinical decision support systems have been advocated to improve the management of patients at risk for CHD but it is unclear whether such systems reduce patients' risk for CHD. METHODS: We conducted a cluster trial within the Swiss HIV Cohort Study (SHCS) of HIV-infected patients, aged 18 years or older, not pregnant and receiving cART for >3 months. We randomized 165 physicians to either guidelines for CHD risk factor management alone or guidelines plus CHD risk profiles. Risk profiles included the Framingham risk score, CHD drug prescriptions and CHD events based on biannual assessments, and were continuously updated by the SHCS data centre and integrated into patient charts by study nurses. Outcome measures were total cholesterol, systolic and diastolic blood pressure and Framingham risk score. RESULTS: A total of 3,266 patients (80% of those eligible) had a final assessment of the primary outcome at least 12 months after the start of the trial. Mean (95% confidence interval) patient differences where physicians received CHD risk profiles and guidelines, rather than guidelines alone, were total cholesterol -0.02 mmol/l (-0.09-0.06), systolic blood pressure -0.4 mmHg (-1.6-0.8), diastolic blood pressure -0.4 mmHg (-1.5-0.7) and Framingham 10-year risk score -0.2% (-0.5-0.1). CONCLUSIONS: Systemic computerized routine provision of CHD risk profiles in addition to guidelines does not significantly improve risk factors for CHD in patients on cART.

MANAGING STRATEGIC CHANGE:New Business Models Applying Wireless Technology as a Source of Competitive Edge

The diffusion of mobile telephony began in 1971 in Finland, when the first car phones, called ARP1 were taken to use. Technologies changed from ARP to NMT and later to GSM. The main application of the technology, however, was voice transfer. The birth of the Internet created an open public data network and easy access to other types of computer-based services over networks. Telephones had been used as modems, but the development of the cellular technologies enabled automatic access from mobile phones to Internet. Also other wireless technologies, for instance Wireless LANs, were also introduced. Telephony had developed from analog to digital in fixed networks and allowed easy integration of fixed and mobile networks. This development opened a completely new functionality to computers and mobile phones. It also initiated the merger of the information technology (IT) and telecommunication (TC) industries. Despite the arising opportunity for firms' new competition the applications based on the new functionality were rare. Furthermore, technology development combined with innovation can be disruptive to industries. This research focuses on the new technology's impact on competition in the ICT industry through understanding the strategic needs and alternative futures of the industry's customers. The change speed inthe ICT industry is high and therefore it was valuable to integrate the DynamicCapability view of the firm in this research. Dynamic capabilities are an application of the Resource-Based View (RBV) of the firm. As is stated in the literature, strategic positioning complements RBV. This theoretical framework leads theresearch to focus on three areas: customer strategic innovation and business model development, external future analysis, and process development combining these two. The theoretical contribution of the research is in the development of methodology integrating theories of the RBV, dynamic capabilities and strategic positioning. The research approach has been constructive due to the actual managerial problems initiating the study. The requirement for iterative and innovative progress in the research supported the chosen research approach. The study applies known methods in product development, for instance, innovation process in theGroup Decision Support Systems (GDSS) laboratory and Quality Function Deployment (QFD), and combines them with known strategy analysis tools like industry analysis and scenario method. As the main result, the thesis presents the strategic innovation process, where new business concepts are used to describe the alternative resource configurations and scenarios as alternative competitive environments, which can be a new way for firms to achieve competitive advantage in high-velocity markets. In addition to the strategic innovation process as a result, thestudy has also resulted in approximately 250 new innovations for the participating firms, reduced technology uncertainty and helped strategic infrastructural decisions in the firms, and produced a knowledge-bank including data from 43 ICT and 19 paper industry firms between the years 1999 - 2004. The methods presentedin this research are also applicable to other industries.

On-Line Analytical Processing of Inventory Turnover

Operatiivisen tiedon tuottaminen loppukäyttäjille analyyttistä tarkastelua silmällä pitäen aiheuttaa ongelmia useille yrityksille. Diplomityö pyrkii ratkaisemaan ko. ongelman Teleste Oyj:ssä. Työ on jaettu kolmeen pääkappaleeseen. Kappale 2 selkiyttää On-Line Analytical Processing (OLAP)- käsitteen. Kappale 3 esittelee muutamia OLAP-tuotteiden valmistajia ja heidän arkkitehtuurejaan sekä tyypillisten sovellusalueiden lisäksi huomioon otettavia asioita OLAP käyttöönoton yhteydessä. Kappale 4, tuo esille varsinaisen ratkaisun. Teknisellä arkkitehtuurilla on merkittävä asema ratkaisun rakenteen kannalta. Tässä on sovellettu Microsoft:n tietovarasto kehysrakennetta. Kappaleen 4 edetessä, tapahtumakäsittelytieto muutetaan informaatioksi ja edelleen loppukäyttäjien tiedoksi. Loppukäyttäjät varustetaan tehokkaalla ja tosiaikaisella analysointityökalulla moniulotteisessa ympäristössä. Vaikka kiertonopeus otetaan työssä sovellusesimerkiksi, työ ei pyri löytämään optimaalista tasoa Telesten varastoille. Siitä huolimatta eräitä parannusehdotuksia mainitaan.

ASTRAL-R score predicts non-recanalisation after intravenous thrombolysis in acute ischaemic stroke.

Intravenous thrombolysis (IVT) as treatment in acute ischaemic strokes may be insufficient to achieve recanalisation in certain patients. Predicting probability of non-recanalisation after IVT may have the potential to influence patient selection to more aggressive management strategies. We aimed at deriving and internally validating a predictive score for post-thrombolytic non-recanalisation, using clinical and radiological variables. In thrombolysis registries from four Swiss academic stroke centres (Lausanne, Bern, Basel and Geneva), patients were selected with large arterial occlusion on acute imaging and with repeated arterial assessment at 24 hours. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. Performance of integer-based predictive model was assessed by bootstrapping available data and cross validation (delete-d method). In 599 thrombolysed strokes, five variables were identified as independent predictors of absence of recanalisation: Acute glucose > 7 mmol/l (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), large Arterial occlusion (A) and decreased Level of consciousness (L). All variables were weighted 1, except for (L) which obtained 2 points based on β-coefficients on the logistic scale. ASTRAL-R scores 0, 3 and 6 corresponded to non-recanalisation probabilities of 18, 44 and 74 % respectively. Predictive ability showed AUC of 0.66 (95 %CI, 0.61-0.70) when using bootstrap and 0.66 (0.63-0.68) when using delete-d cross validation. In conclusion, the 5-item ASTRAL-R score moderately predicts non-recanalisation at 24 hours in thrombolysed ischaemic strokes. If its performance can be confirmed by external validation and its clinical usefulness can be proven, the score may influence patient selection for more aggressive revascularisation strategies in routine clinical practice.

Tehdasalueiden liikenneturvallisuutta kehittävä malli

Työn tavoitteena oli antaa tietoa tehdasalueiden turvallisuuteen vaikuttavista tekijöistä ja luoda malli tehdasalueen liikenteen turvallisuuden parantamiseen päätöksenteon tukivälineitä apuna käyttäen. Työ sai alkunsa todellisen turvallisuutta parantavan investoinnin analysointi-tarpeesta. Aluksi työssä perehdytään yleisesti tehdasalueiden turvallisuuteen tarkastelemalla työturvallisuuden, riskienhallinnan ja turvallisuusjohtamisen asemaa, mittaamista ja taloudellisia vaikutuksia yrityksen toiminnalle. Yleiskatsaus tehdasalueiden turvallisuuteen antaa kuvan, millaiseen toimintaympäristöön turvallisuutta parantavaa mallia ollaan kehittämässä. Malli koostuu viidestä vaiheesta, joita voidaan käyttää kokonaisuutena tai toisistaan erillään. Ensin selvitään tutkittavan alueen kulkuväylät ja liikenne sekä mallinnetaan se. Tämän jälkeen kartoitetaan ongelmakohdat ja etsitään sopivia vaihtoehtoja niiden turvallisuuden parantamiseksi. Vaihtoehtoja analysoidaan SWOT-menetelmän avulla. Turvallisuutta parantavien investointien arvioimiseen esitetään muutamia mittareita, joita voidaan käyttää hankintojen arvioimiseen. Viimeisessä vaiheessa tutustutaan päätöksenteon tukisysteemeihin ja esitetään tietokoneavusteinen päätöksentekomenetelmä AHP. Käytännön soveltaminen on esitetty esimerkein mallin eri vaiheiden yhteydessä.Malli on tarkoitettu suunnittelijoiden, johdon ja työsuojelun työkaluksi, jonka avulla voidaan parantaa tehdasympäristön liikenteen tehokkuutta ja turvallisuutta sekä tutustuttaa käyttäjä päätöksentekomenetelmiin.

SQualTrack: A Tool for Robust Fault Detection

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the system's behavior, obtained from the measurements, and the model's behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is semiqualitative tracking (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project advanced decision support system for chemical/petrochemical manufacturing processes and are also described in this paper

An analytic approach to distribution logistics strategic management

Logistics management is increasingly being recognised by many companies to be of critical concern. The logistics function includes directly or indirectly many of the new areas for achieving or maintaining competitive advantage that companies have been forced to develop due to increasing competitive pressures. The key to achieving a competitive advantage is to manage the logistics function strategically which involves determining the most cost effective method of providing the necessary customer service levels from the many combinations of operating procedures in the areas of transportation, warehousing, order processing and information systems, production, and inventory management. In this thesis, a comprehensive distribution logistics strategic management process is formed by integrating the periodic strategic planning process with a continuous strategic issues management process. Strategic planning is used for defining the basic objectives for a company and assuring co operation and synergy between the different functions of a company while strategic issues management is used on a continuous basis in order to deal with environmental and internal turbulence. The strategic planning subprocess consists of the following main phases: (1) situational analyses, (2) defining the vision and strategic goals for the logistics function, (3) determining objectives and strategies, (4) drawing up tactical action plans, and (5) evaluating the implementation of the plans and making the needed adjustments. The aim of the strategic issues management subprocess is to continuously scan the environment and the organisation for early identification of the issues having a significant impact on the logistics function using the following steps: (1) the identification of trends, (2) assessing the impact and urgency of the identified trends, (3) assigning priorities to the issues, and (4) planning responses to the, issues. The Analytic Hierarchy Process (AHP) is a systematic procedure for structuring any problem. AHP is based on the following three principles: decomposition, comparative judgements, and synthesis of priorities. AHP starts by decomposing a complex, multicriteria problem into a hierarchy where each level consists of a few manageable elements which are then decomposed into another set of elements. The second step is to use a measurement methodology to establish priorities among the elements within each level of the hierarchy. The third step in using AHP is to synthesise the priorities of the elements to establish the overall priorities for the decision alternatives. In this thesis, decision support systems are developed for different areas of distribution logistics strategic management by applying the Analytic Hierarchy Process. The areas covered are: (1) logistics strategic issues management, (2) planning of logistic structure, (3) warehouse site selection, (4) inventory forecasting, (5) defining logistic action and development plans, (6) choosing a distribution logistics strategy, (7) analysing and selecting transport service providers, (8) defining the logistic vision and strategic goals, (9) benchmarking logistic performance, and (10) logistic service management. The thesis demonstrates the potential of AHP as a systematic and analytic approach to distribution logistics strategic management.