Wyniki wyszukiwania - BazTech

1

Public Health Professionals and evidence based medicine

Rusnák M., Grendová K.

Journal of Ecology and Health

|

2011

|

R. 15, nr 5

254-258

EN

The aim of this paper is to inform the reader of the basic principles of evaluation of evidence in medicine and their use for improving the quality of health care and care of patients. The reader also gets an overview of existing evidence, the strength of the evidence, evaluating evidence, guidelines and quality improvement projects in their implementation and measurement of their usefulness.

PL

Celem niniejszej pracy jest poinformowanie czytelnika o podstawowych zasadach oceny roli faktów w medycynie i jej zastosowanie w celu poprawy jakości ochrony zdrowia pacjentów. Czytający dodatkowo ma szansę zaznajomić się z istniejącymi już faktami, ich znaczeniem i oceną oraz wytycznymi i projektami poprawy jakości, ich wdrożeniem i oceną ich użyteczności.

2

Designing a protocol to aid decision making in a billion dollar healthcare organisation: using collaborative research

Lane V., Avalon J., Hayward P., Snaith J.

Journal of Medical Informatics & Technologies

|

2010

|

Vol. 15

93--100

EN

This paper describes a method to accomplish 'a pre-planned guided search of interesting and known knowledge and information sources for decision making in public health medicine', i.e. a search protocol. The protocol is based around a knowledge base of known information and knowledge sources. These sources have been useful in earlier public health studies. The database of sources is maintained and updated as a consequence of each new study on which the search protocol is used. The paper outlines how the protocol has been created, built and tested. The results of using the protocol on five different public health studies are presented and analysed.

3

Is knowledge management nonsense or the foundation of quality healthcare?

Lane V., Snaith J., Lane D.

Journal of Medical Informatics & Technologies

|

2006

|

Vol. 10

11--22

EN

This paper explains the concepts of knowledge management and how these apply to healthcare. It then presents a number of knowledge management case studies in order to examine how and where knowledge management initiatives might bring benefits to healthcare organisations. In order to present a more complete picture of knowledge management, the arguments of a number of critics of the knowledge management approach are presented and analysed.

4

Constructing a Decision Tree for Graph-Structured Data and its Applications

Geamsakul W., Yoshida T., Ohara K., Motoda H., Yokoi H., Takabayashi K.

Fundamenta Informaticae

|

2005

|

Vol. 66, nr 1,2

131--160

EN

A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from graph-structured data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search. Meanwhile, a decision tree is an effective means of data classification from which rules that are easy to understand can be obtained. However, a decision tree could not be constructed for the data which is not explicitly expressed with attribute-value pairs. This paper proposes a method called Decision Tree Graph-Based Induction (DT-GBI), which constructs a classifier (decision tree) for graph-structured data while simultaneously constructing attributes for classification using GBI. Substructures (patterns) are extracted at each node of a decision tree by stepwise pair expansion in GBI to be used as attributes for testing. Since attributes (features) are constructed while a classifier is being constructed, DT-GBI can be conceived as a method for feature construction. The predictive accuracy of a decision tree is affected by which attributes (patterns) are used and how they are constructed. A beam search is employed to extract good enough discriminative patterns within the greedy search framework. Pessimistic pruning is incorporated to avoid overfitting to the training data. Experiments using a DNA dataset were conducted to see the effect of the beam width and the number of chunking at each node of a decision tree. The results indicate that DT-GBI that uses very little prior domain knowledge can construct a decision tree that is comparable to other classifiers constructed using the domain knowledge. DT-GBI was also applied to analyze a real-world hepatitis dataset as a part of evidence-based medicine. Four classification tasks of the hepatitis data were conducted using only the time-series data of blood inspection and urinalysis. The preliminary results of experiments, both constructed decision trees and their predictive accuracies as well as extracted patterns, are reported in this paper. Some of the patterns match domain experts' experience and the overall results are encouraging.