By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Health and Medical Informatics. According to news reporting from Pittsburgh, Pennsylvania, by VerticalNews journalists, research stated, "Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models."
The news correspondents obtained a quote from the research from the University of Pittsburgh, "In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small."
According to the news reporters, the research concluded: "A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data."
For more information on this research see: Learning classification models with soft-label information. Journal of the American Medical Informatics Association, 2014;21(3):501-508. Journal of the American Medical Informatics Association can be contacted at: Bmj Publishing Group, British Med Assoc House, Tavistock Square, London WC1H 9JR, England.
Our news journalists report that additional information may be obtained by contacting Q. Nguyen, University of Pittsburgh, Dept. of Comp Sci, Pittsburgh, PA 15260, United States. Additional authors for this research include H. Valizadegan and M. Hauskrecht.
Keywords for this news article include: Pittsburgh, Pennsylvania, United States, North and Central America, Health and Medical Informatics
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