Oops! Another medication error : a literature review of contributing factors and methods to prevent medication errors

The purpose of this bachelor's thesis was to chart scientific research articles to present contributing factors to medication errors done by nurses in a hospital setting, and introduce methods to prevent medication errors. Additionally, international and Finnish research was combined and findings were reflected in relation to the Finnish health care system. Literature review was conducted out of 23 scientific articles. Data was searched systematically from CINAHL, MEDIC and MEDLINE databases, and also manually. Literature was analysed and the findings combined using inductive content analysis. Findings revealed that both organisational and individual factors contributed to medication errors. High workload, communication breakdowns, unsuitable working environment, distractions and interruptions, and similar medication products were identified as organisational factors. Individual factors included nurses' inability to follow protocol, inadequate knowledge of medications and personal qualities of the nurse. Developing and improving the physical environment, error reporting, and medication management protocols were emphasised as methods to prevent medication errors. Investing to the staff's competence and well-being was also identified as a prevention method. The number of Finnish articles was small, and therefore the applicability of the findings to Finland is difficult to assess. However, the findings seem to fit to the Finnish health care system relatively well. Further research is needed to identify those factors that contribute to medication errors in Finland. This is a necessity for the development of methods to prevent medication errors that fit in to the Finnish health care system.

Metal surface and subsurface inspection using nondestructive optical methods

This thesis considers nondestructive optical methods for metal surface and subsurface inspection. The main purpose of this thesis was to study some optical methods in order to find out their applicability to industrial measurements. In laboratory testing the simplest light scattering approach, measurement of specular reflectance, was used for surface roughness evaluation. Surface roughness, curvature and finishing process of metal sheets were determined by specular reflectance measurements. Using a fixed angleof incidence, the specular reflectance method might be automated for industrialinspection. For defect detection holographic interferometry and thermography were compared. Using either holographic interferometry or thermography, relativelysmall-size defects in metal plates could be revealed. Holographic techniques have some limitations for industrial measurements. On the contrary, thermography has excellent prospects for on-line inspection, especially with scanning techniques.

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.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Some advances in mathematical models for preference relations

Preference relations, and their modeling, have played a crucial role in both social sciences and applied mathematics. A special category of preference relations is represented by cardinal preference relations, which are nothing other than relations which can also take into account the degree of relation. Preference relations play a pivotal role in most of multi criteria decision making methods and in the operational research. This thesis aims at showing some recent advances in their methodology. Actually, there are a number of open issues in this field and the contributions presented in this thesis can be grouped accordingly. The first issue regards the estimation of a weight vector given a preference relation. A new and efficient algorithm for estimating the priority vector of a reciprocal relation, i.e. a special type of preference relation, is going to be presented. The same section contains the proof that twenty methods already proposed in literature lead to unsatisfactory results as they employ a conflicting constraint in their optimization model. The second area of interest concerns consistency evaluation and it is possibly the kernel of the thesis. This thesis contains the proofs that some indices are equivalent and that therefore, some seemingly different formulae, end up leading to the very same result. Moreover, some numerical simulations are presented. The section ends with some consideration of a new method for fairly evaluating consistency. The third matter regards incomplete relations and how to estimate missing comparisons. This section reports a numerical study of the methods already proposed in literature and analyzes their behavior in different situations. The fourth, and last, topic, proposes a way to deal with group decision making by means of connecting preference relations with social network analysis.

Software development methods and quality assurance: Special focus on South Korea

The purpose of this study was to explore software development methods and quality assurance practices used by South Korean software industry. Empirical data was collected by conducting a survey that focused on three main parts: software life cycle models and methods, software quality assurance including quality standards, the strengths and weaknesses of South Korean software industry. The results of the completed survey showed that the use of agile methods is slightly surpassing the use of traditional software development methods. The survey also revealed an interesting result that almost half of the South Korean companies do not use any software quality assurance plan in their projects. For the state of South Korean software industry large number of the respondents thought that despite of the weakness, the status of software development in South Korea will improve in the future.