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CONCLUSIONS: The current study used the largest all-payer hospital based emergency department dataset to provide national estimates of firearm related injuries amongst children in the United States during the year 2008 and highlights the public health impact of such injuries.
The application of the models was illustrated with an analysis of the Ontario newborn registry and an emergency department dataset.
We evaluate the proposed algorithm on a real dataset from the department of dermatology and venereology of a large local hospital.
Several regional syndromic surveillance systems that focus on real time clinical and administrative emergency department datasets have been deployed [ 15- 17].
If text searching is to be used to increase the injury surveillance information by enabling use of routine emergency department datasets, then it is essential to quantify the performance of the different methods.
We appreciate the suggestion of the reviewer for extending the validation to other datasets, but our department is heavily working within the field of Neurodegeneration, we would like to limit ourselves to diseases that fall into this domain.
The department's dataset covered originally 20,034 patients.
Survival data were obtained by integrating the department's dataset with the nationwide, centrally-curated administrative PESEL database.
We applied our method to predict the KEGG pathway membership for a microarray dataset produced in our department: human MCF-7 breast cancer cells were treated with 100 nM tamoxifen for 48 h.
The total dataset of the inpatient departments consisted of 58,055 patients, the dataset of outpatients of 79,498 patients (see Additional file 1: Table S1).
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