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The proposed algorithm also holds for patients treated with peg-interferon and ribavirin.
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The proposed algorithm was performed for all participants in this demonstration study.
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Results: The proposed algorithm dominated the current algorithm for prenatal diagnosis.
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The proposed algorithm has a low-order polynomial time complexity and is computationally efficient.
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Herein, the literature is reviewed, and a proposed algorithm for HSPC mobilization is presented.
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In comparison with some recently developed methods, the proposed algorithm also achieves a good performance.
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Our proposed algorithm can assist in determining which patients can be treated with antibiotics alone.
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The proposed algorithm also has the advantage of providing an estimate of the underlying probability.
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Experimental results demonstrate that the proposed algorithm outperforms the popular fusion algorithm based on wavelet transform.
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An L-shaped head and neck tumor was used to demonstrate the validity of the proposed algorithm.
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The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA.
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The proposed algorithm is computationally efficient.
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The proposed algorithm has been developed into a free standing software application (GADA, Genome Alteration Detection Algorithm).
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Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.
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Conclusion: By switching to the proposed algorithm, cost saving can be achieved whilst maximizing the diagnostic rate for invasive prenatal diagnosis.
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This study aims to evaluate the cost-effectiveness of the proposed algorithm versus the current algorithm for prenatal diagnosis in Hong Kong.