A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions

Medical decision support using machine learning for early detection of late-onset neonatal sepsis.

Objectives
The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.

/pdf/a-comparison-of-intensive-care-unit-mortality-prediction-2menct15o5.pdf

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques.

Our ultimate objective in data mining is to identify any hidden patterns or relationships between our data elements, and in one sense, machine learning provides us with a set of techniques to do just that: techniques that allow us to learn the patterns without any outside influence (unsupervised learning). However, just as is the case with anything, that power comes at a price, but the results can be very interesting and very significant. In other cases, we have some sense on what the results should be and so can guide the learning techniques through an initial “training” phase, directing our system and honing the results (supervised learning). These two broad classifications of machine-learning methods will ground us as we discuss a broad range of techniques and where they are currently being applied in life sciences research, expanding our toolkit and enabling us to take a very different path in our analysis efforts: using an artificial intelligence discipline and letting the data tell us what it contains. As datasets grow, these techniques become more important.

Machine-Learning Techniques

The invention provides a Web service which enables a publisher to share his digital resources such as an address card or a calendar with a number of subscribers based on different sharing relationships. The Web service includes a host-based interface called “My Community”, for example, with which the publisher manages the share-relationships with his community members. The community members are organized into different groups. Each group includes a number of community members who have a common sharing relationship with the publisher with respect to one or more views of the shared resources. A resource may have multiple views. Each of the views has Metadata describing sharing-styles, as well as version, creation date, size, and the like. Each sharing style corresponds to a specific sharing relationship between a community member and the publisher.

System and method for community centric resource sharing based on a publishing subscription model

Erratum to Knowledge discovery and knowledge validation in intensive care

https://www.econstor.eu/bitstream/10419/77225/2/2000-14.pdf

Knowledge discovery and knowledge validation in intensive care

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Erratum to Knowledge discovery and knowledge validation in intensive care

Knowledge discovery and knowledge validation in intensive care