Smartphone-based analysis of biochemical tests for health monitoring support at home.

In the context of home-based healthcare monitoring systems, it is desirable that the results obtained from biochemical tests - tests of various body fluids such as blood and urine - are objective and automatically generated to reduce the number of man-made errors. The authors present the StripTest reader - an innovative smartphone-based interpreter of biochemical tests based on paper-based strip colour using image processing techniques. The working principles of the reader include image acquisition of the colour strip pads using the camera phone, analysing the images within the phone and comparing them with reference colours provided by the manufacturer to obtain the test result.

Learning Bayesian networks for clinical time series analysis.

Introduction: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.

On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks.

Objectives: To obtain a balanced view on the role and place of expert knowledge and learning methods in building Bayesian networks for medical image interpretation.

Methods And Materials: The interpretation of mammograms was selected as the example medical image interpretation problem. Medical image interpretation has its own common standards and procedures.

A probabilistic framework for image information fusion with an application to mammographic analysis.

The recent increased interest in information fusion methods for solving complex problem, such as in image analysis, is motivated by the wish to better exploit the multitude of information, available from different sources, to enhance decision-making. In this paper, we propose a novel method, that advances the state of the art of fusing image information from different views, based on a special class of probabilistic graphical models, called causal independence models. The strength of this method is its ability to systematically and naturally capture uncertain domain knowledge, while performing information fusion in a computationally efficient way.

Using local context information to improve automatic mammographic mass detection.

Despite their promising application, current Computer-Aided Detection (CAD) systems face difficulties, especially in the detection of malignant masses -a major mammographic sign for breast cancer. One of the main problems is the large number of false positives prompted, which is a critical issue in screening programs where the number of normal cases is considerably large. A crucial determinant for this problem is the dependence of the CAD output on the single pixel-based locations initially detected.

Monotone and partially monotone neural networks.

In many classification and prediction problems it is known that the response variable depends on certain explanatory variables. Monotone neural networks can be used as powerful tools to build monotone models with better accuracy and lower variance compared to ordinary nonmonotone models. Monotonicity is usually obtained by putting constraints on the parameters of the network.

Comparison of universal approximators incorporating partial monotonicity by structure.

Neural networks applied in control loops and safety-critical domains have to meet more requirements than just the overall best function approximation. On the one hand, a small approximation error is required; on the other hand, the smoothness and the monotonicity of selected input-output relations have to be guaranteed. Otherwise, the stability of most of the control laws is lost.

Improved mammographic CAD performance using multi-view information: a Bayesian network framework.

Mammographic reading by radiologists requires the comparison of at least two breast projections (views) for the detection and the diagnosis of breast abnormalities. Despite their reported potential to support radiologists, most mammographic computer-aided detection (CAD) systems have a major limitation: as opposed to the radiologist's practice, computerized systems analyze each view independently. To tackle this problem, in this paper, we propose a Bayesian network framework for multi-view mammographic analysis, with main focus on breast cancer detection at a patient level.