An ANN model for treatment prediction in HBV patients |
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Authors: | Iqbal Sajid Masood Khalid Jafer Osman |
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Affiliation: | 1Bioinformatics lab, National Center of Excellence in Molecular Biology, Lahore, Pakistan;2Central veterinary research laboratory, Department of molecular biology and genetics, Dubai, United Arab Emirates |
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Abstract: | Two types of antiviral treatments, namely, interferon and nucleoside/nucleotide analogues are available for hepatitis infections. The selection of drug and dose determined using known pharmacokinetics and pharmacodynamics data is important. The lack of sufficient information for pharmacokinetics of a drug may not produce the desired results. Artificial neural network (ANN) provides a novel model-independent approach to pharmacokinetics and pharmacodynamics data. ANN model is created by supervised learning of 90 patients sample to predict the treatment strategy (lamivudine only and Lamivudine + Interferon) on the basis of viral load, liver function test, visit number, treatment duration, ethnic area, sex, and age. The model was trained with 68 (77.3%) samples and tested with 20 (22.7%) samples. The model produced 92% accuracy with 92.8% sensitivity and 83.3% specificity. |
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Keywords: | ANN (Artificial neural networks) Hepatitis Prediction Treatment |
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