6,408 research outputs found
Application of machine learning for hematological diagnosis
Quick and accurate medical diagnosis is crucial for the successful treatment
of a disease. Using machine learning algorithms, we have built two models to
predict a hematologic disease, based on laboratory blood test results. In one
predictive model, we used all available blood test parameters and in the other
a reduced set, which is usually measured upon patient admittance. Both models
produced good results, with a prediction accuracy of 0.88 and 0.86, when
considering the list of five most probable diseases, and 0.59 and 0.57, when
considering only the most probable disease. Models did not differ significantly
from each other, which indicates that a reduced set of parameters contains a
relevant fingerprint of a disease, expanding the utility of the model for
general practitioner's use and indicating that there is more information in the
blood test results than physicians recognize. In the clinical test we showed
that the accuracy of our predictive models was on a par with the ability of
hematology specialists. Our study is the first to show that a machine learning
predictive model based on blood tests alone, can be successfully applied to
predict hematologic diseases and could open up unprecedented possibilities in
medical diagnosis.Comment: 15 pages, 6 figure
Quantifying the Detrimental Impacts of Land-Use and Management Change on European Forest Bird Populations
The ecological impacts of changing forest management practices in Europe are poorly understood despite European forests being highly managed. Furthermore, the effects of potential drivers of forest biodiversity decline are rarely considered in concert, thus limiting effective conservation or sustainable forest management. We present a trait-based framework that we use to assess the detrimental impact of multiple land-use and management changes in forests on bird populations across Europe. Major changes to forest habitats occurring in recent decades, and their impact on resource availability for birds were identified. Risk associated with these changes for 52 species of forest birds, defined as the proportion of each species' key resources detrimentally affected through changes in abundance and/or availability, was quantified and compared to their pan-European population growth rates between 1980 and 2009. Relationships between risk and population growth were found to be significantly negative, indicating that resource loss in European forests is an important driver of decline for both resident and migrant birds. Our results demonstrate that coarse quantification of resource use and ecological change can be valuable in understanding causes of biodiversity decline, and thus in informing conservation strategy and policy. Such an approach has good potential to be extended for predictive use in assessing the impact of possible future changes to forest management and to develop more precise indicators of forest health
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