<mets:mets OBJID="eprint_13614" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2024-01-01T20:41:41Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>IIASA Repository</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_13614_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>Nonlinear models and data analysis of cancer patients</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">A.</mods:namePart><mods:namePart type="family">Asachenkov</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>New methods for analyzing clinical data for oncological patients are developed. These data are considered as observed variables of the mathematical tumor model. The connection between a mortality index and individual dynamics of the observed data is discussed. This approach allows the clinically unexpressed part of the tumor process to be observed, and it also allows the process to be examined during treatment.</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1989-05</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>IEEE</mods:publisher></mods:originInfo><mods:genre>Book Section</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_13614"><mets:rightsMD ID="rights_eprint_13614_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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