Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Towards a user-friendly webservice architecture for statistical machine translation in the PANACEA project

This paper presents a webservice architecture for Statistical Machine Translation aimed at non-technical users. A workfloweditor allows a user to combine different webservices using a graphical user interface. In the current state of this project,the webservices have been implemented for a range of sentential and sub-sententialaligners. The advantage of a common interface and a common data format allows the user to build workflows exchanging different aligners.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Automatic acquisition of Named Entities for Rule-Based Machine Translation

This paper proposes to enrich RBMTdictionaries with Named Entities(NEs) automatically acquired fromWikipedia. The method is appliedto the Apertium English-Spanishsystem and its performance comparedto that of Apertium with and withouthandtagged NEs. The system withautomatic NEs outperforms the onewithout NEs, while results vary whencompared to a system with handtaggedNEs (results are comparable forSpanish to English but slightly worstfor English to Spanish). Apart fromthat, adding automatic NEs contributesto decreasing the amount of unknownterms by more than 10%.

Taking on new challenges in multi-word unit processing for Machine Translation

This paper discusses the qualitativecomparative evaluation performed on theresults of two machine translation systemswith different approaches to the processing ofmulti-word units. It proposes a solution forovercoming the difficulties multi-word unitspresent to machine translation by adopting amethodology that combines the lexicongrammar approach with OpenLogos ontologyand semantico-syntactic rules. The paper alsodiscusses the importance of a qualitativeevaluation metrics to correctly evaluate theperformance of machine translation engineswith regards to multi-word units.

A Widely Used Machine Translation Service and its Migration to a Free/Open-Source Solution : the Case of Softcatalà

Softcatalà is a non-profit associationcreated more than 10 years ago to fightthe marginalisation of the Catalan languagein information and communicationtechnologies. It has led the localisationof many applications and thecreation of a website which allows itsusers to translate texts between Spanishand Catalan using an external closed-sourcetranslation engine. Recently,the closed-source translation back-endhas been replaced by a free/open-sourcesolution completely managed by Softcatalà: the Apertium machine translationplatform and the ScaleMT web serviceframework. Thanks to the opennessof the new solution, it is possibleto take advantage of the huge amount ofusers of the Softcatalà translation serviceto improve it, using a series ofmethods presented in this paper. In addition,a study of the translations requestedby the users has been carriedout, and it shows that the translationback-end change has not affected theusage patterns.

Energy-based modelling and simulation of the interconnection of a back-to-back converter and a doubly-fed induction machine

This paper describes the port interconnection of two subsystems: a power electronics subsystem (a back-to-back AC/CA converter (B2B), coupled to a phase of the power grid), and an electromechanical subsystem (a doubly-fed induction machine (DFIM). The B2B is a variable structure system (VSS), due to presence of control-actuated switches: however, from a modelling simulation, as well as a control-design, point of view, it is sensible to consider modulated transformers (MTF in the bond graph language) instead of the pairs of complementary switches. The port-Hamiltonian models of both subsystems are presented and, using a power-preserving interconnection, the Hamiltonian description of the whole system is obtained; detailed bond graphs of all subsystems and the complete system are also provided. Using passivity-based controllers computed in the Hamiltonian formalism for both subsystems, the whole model is simulated; simulations are run to rest the correctness and efficiency of the Hamiltonian network modelling approach used in this work.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Paper Surface Image Processing for Roughness Quality Measurement

Diplomityössä on käsitelty paperin pinnankarkeuden mittausta, joka on keskeisimpiä ongelmia paperimateriaalien tutkimuksessa. Paperiteollisuudessa käytettävät mittausmenetelmät sisältävät monia haittapuolia kuten esimerkiksi epätarkkuus ja yhteensopimattomuus sileiden papereiden mittauksissa, sekä suuret vaatimukset laboratorio-olosuhteille ja menetelmien hitaus. Työssä on tutkittu optiseen sirontaan perustuvia menetelmiä pinnankarkeuden määrittämisessä. Konenäköä ja kuvan-käsittelytekniikoita tutkittiin karkeilla paperipinnoilla. Tutkimuksessa käytetyt algoritmit on tehty Matlab® ohjelmalle. Saadut tulokset osoittavat mahdollisuuden pinnankarkeuden mittaamiseen kuvauksen avulla. Parhaimman tuloksen perinteisen ja kuvausmenetelmän välillä antoi fraktaaliulottuvuuteen perustuva menetelmä.

Methods of Jet to Wire Speed Ratio Determination and Its Effect in Paper and Board Manufacture

Suihku/viira-nopeussuhde on perälaatikon huulisuihkun ja viiran välinen nopeusero. Se vaikuttaa suuresti paperin ja kartongin loppuominaisuuksiin, kuten formaatioon sekä kuituorientaatioon ja näin ollen paperin lujuusominaisuuksiin. Tämän johdosta on erityisen tärkeää tietää todellinen suihku/viira-nopeussuhde paperin- ja kartonginvalmistuksessa. Perinteinen suihku/viira-nopeussuhteen määritysmenetelmä perustuu perälaatikon kokonaispaineeseen. Tällä menetelmällä kuitenkin todellinen huulisuihkun nopeus saattaa usein jäädä tietämättä johtuen mahdollisesta virheellisestä painemittarin kalibroinnista sekä laskuyhtälön epätarkkuuksista. Tämän johdosta on kehitetty useita reaaliaikaisia huulisuihkun mittausmenetelmiä. Perälaatikon parametrien optimaaliset asetukset ovat mahdollista määrittää ja ylläpitää huulisuihkun nopeuden “on-line” määrityksellä. Perälaatikon parametrejä ovat mm. huulisuihku, huuliaukon korkeusprofiili, reunavirtaukset ja syöttövirtauksen tasaisuus. Huulisuihkun nopeuden on-line mittauksella paljastuu myös muita perälaatikon ongelmakohtia, kuten mekaaniset viat, joita on perinteisesti tutkittu aikaa vievillä paperin ja kartongin lopputuoteanalyyseillä.

Offset-print mottling measurement using machine vision

Mottling is one of the key defects in offset-printing. Mottling can be defined as unwanted unevenness of print. In this work, diameter of a mottle spot is defined between 0.5-10.0 mm. There are several types of mottling, but the reason behind the problem is still not fully understood. Several commercial machine vision products for the evaluation of print unevenness have been presented. Two of these methods used in these products have been implemented in this thesis. The one is the cluster method and the other is the band-pass method. The properties of human vision system have been taken into account in the implementation of these two methods. An index produced by the cluster method is a weighted sum of the number of found spots, and an index produced by band-pass method is a weighted sum of coefficients of variations of gray-levels for each spatial band. Both methods produce larger indices for visually poor samples, so they can discern good samples from the poor ones. The difference between the indices for good and poor samples is slightly larger produced by the cluster method. 11 However, without the samples evaluated by human experts, the goodness of these results is still questionable. This comparison will be left to the next phase of the project.

Radanohjaimen suunnittelu leveille päällystyskoneille

Radanohjaimia käytetään radan poikkisuuntaiseen paikoitukseen sekä oskillointiin ja ne ovat osa paperinvalmistuslinjojen radanhallintajärjestelmiä. Oikein suunnitellulla ja sijoitetulla radanohjaimella voidaan estää haitallisten ratasiirtymien esiintyminen, pienentää hylkymateriaalin määrää sekä parantaa rullauksen laatua. Ohjausperiaatteet sekä niihin liittyvä laitetekniikka on tunnettu jo vuosikymmenten ajan, joskin todelliset radanohjaustarpeet ja -laitteet ovat tähän asti rajoittuneet paino-, tekstiili- ja metalliteollisuudessa käytössä oleville kapeille radoille. Tämän työn tavoitteena oli selvittää 5… 12 metriä leveälle, modernissa päällystyskoneessa kulkevalle paperiradalle soveltuvat ohjausperiaatteet sekä muodostaa radan todennäköisimpiin vauriomuotoihin perustuvat suunnittelukriteerit kahdelle erityyppiselle ohjainkonstruktiolle. Yksitelainen, taivuttava radanohjain soveltuu kohteisiin, joissa ohjainta edeltää suhteellisen pitkä vapaa ratavienti. Kaksitelainen, taittava radanohjain on puolestaan sijoitettavissa huomattavasti lyhyempään ratavientiin. Radanohjauksen teoria pohjautuu pitävän telan ja radan väliseen kohtisuoran tulokulman periaatteeseen, jonka perusteella mikä tahansa yhdensuuntaisuuspoikkeama kahden telan välillä johtaa radan poikkisuuntaiseen siirtymään. Tämän periaatteen pohjalta voidaan dynaamista ohjaustilannetta approksimoida staattisin menetelmin sekä muodostaa kireysmuutoksiin ja aaltoiluun perustuvat geometrian mitoitusperiaatteet ohjaimen ympäristön ratavienneille. Nopean ohjauksen toteutus edellyttää radanohjaimen liikkeen olevan nivelen ympäri tapahtuvaa yhdistettyä translaatio- ja rotaatioliikettä. Ohjainkonstruktiot suunnitellaan siten, että teorian mukaiset optimaaliset liikeradat toteutuvat vaaditulla ohjausnopeudella. Suunnittelua ohjaavat tuotteille asetetut lujuus- ja värähtelykriteerit sekä aiheeseen liittyvät koneturvallisuusstandardit. Konstruointi suoritetaan järjestelmällisen tuotesuunnitteluprosessin vaiheiden ja menetelmien mukaisesti.