An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Feasibility of an evidence-based literature search service for general practitioners

Objective: To test the feasibility of an evidence-based clinical literature search service to help answer general practitioners' (GPs') clinical questions. Design: Two search services supplied GPs who submitted questions with the best available empirical evidence to answer these questions. The GPs provided feedback on the value of the service, and concordance of answers from the two search services was assessed. Setting: Two literature search services (Queensland and Victoria), operating for nine months from February 1999. Main outcome measures: Use of the service; time taken to locate answers; availability of evidence; value of the service to GPs; and consistency of answers from the two services. Results: 58 GPs asked 160 questions (29 asked one, 11 asked five or more). The questions concerned treatment (65%), aetiology (17%), prognosis (13%), and diagnosis (5%). Answering a question took a mean of 3 hours 32 minutes of personnel time (95% Cl, 2.67-3.97); nine questions took longer than 10 hours each to answer, the longest taking 23 hours 30 minutes. Evidence of suitable quality to provide a sound answer was available for 126 (79%) questions. Feedback data for 84 (53%) questions, provided by 42 GPs, showed that they appreciated the service, and asking the questions changed clinical care. There were many minor differences between the answers from the two centres, and substantial differences in the evidence found for 4/14 questions. However, conclusions reached were largely similar, with no or only minor differences for all questions. Conclusions: It is feasible to provide a literature search service, but further assessment is needed to establish its cost effectiveness.

Cancer culture: Epidemics, human behaviour and the dubious search for new risk factors

The rise of melanoma and the almost complete decline of stomach cancer clearly reflect disturbances of human culture during the 20th century. Environmental factors play a dominant role in the epidemiology of melanoma and many other malignancies.