Premium
Hepatitis C treatment based on differential neural estimator
Author(s) -
CABRERA AGUSTIN IGNACIO,
GOMEZ YOLANDA,
BAUTISTA MARIA ESTHER,
RAMIREZSOTELO MARIA GUADALUPE
Publication year - 2008
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.22.1_supplement.898.44
Subject(s) - estimator , artificial neural network , medicine , population , hepatitis , mathematics , control theory (sociology) , computer science , mathematical optimization , control (management) , statistics , artificial intelligence , immunology , environmental health
Hepatitis C is suffered approximately by the world population's three percent and it has become an important social problem that has shown to be in continuous growing, for each case the treatment is different so a general procedure is not applicable to this sickness. For that reason develop new ways to treatment are needed. This report show the application of the Hepatitis C Virus mathematical model and the differential neural networks (DNN) to develop a system estimator in order to rebuild the states variables of this sickness and the design a control function based on Hamilton Jacobi Technique (HJT), a technique used in control systems. The estimator was trained just one time with the information given of a sick patient without previous treatment. This control is a tool that gives us an approximation of the dosage of Interferon Alpha 2β (IFN‐2β) to be prescribed to a patient in order to maintain the viral load in low levels. This algorithm was tested by numerical simulations and the good performance shows us the possibility to develop Hepatitis C treatments for medications effective dose.