title: Individualized model discovery: the case of anemia patients.
abstract: The universal sequel to chronic kidney condition (CKD) is anemia. Patients of anemia have kidneys that are incapable of performing certain basic functions such as sensing of oxygen levels to secrete erythropoietin when red blood cell counts are low. Under such conditions, external administration of human recombinant erythropoietin (EPO) is administered as alternative to improve conditions of CKD patients by increasing their hemoglobin (Hb) levels to a given therapeutic range. Presently, EPO dosing strategies extensively depend on packet inserts and on "average" responses to the medication from previous patients. Clearly dosage strategies based on these approaches are, at best, nonoptimal to EPO medication and potentially dangerous to patients that do not adhere to the notion of expected "average" response. In this work, a technique called semi-blind robust identification is provided to uniquely identify models of the individual patients of anemia based on their actual Hb responses and EPO administration. Using the a priori information and the measured input-output data of the individual patients, the procedure identifies a unique model consisting of a nominal model and the associated model uncertainty for the patients. By incorporating the effects of unknown system initial conditions, considerably small measurement samples can be used in the modeling process.
authors.affiliation: Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, United States. Electronic address: eakabua@gmail.com.
authors.forename: Elom
authors.initials: E
authors.lastname: Akabua
authors.affiliation: Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, United States.
authors.forename: Tamer
authors.initials: T
authors.lastname: Inanc
authors.affiliation: Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, United States; Department of Medicine, University of Louisville, Louisville, KY 40292, United States.
authors.forename: Adam
authors.initials: A
authors.lastname: Gaweda
authors.affiliation: Department of Medicine, University of Louisville, Louisville, KY 40292, United States; Department of Veteran Affairs, University of Louisville, Louisville, KY 40292, United States.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Brier
authors.affiliation: Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, United States.
authors.forename: Seongho
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, United States; Spoleczna Akademia Nauk, 90-011 Lodz, Poland.
authors.forename: Jacek M
authors.initials: JM
authors.lastname: Zurada
mesh_terms: D000465:Algorithms; D000740:Anemia / Q000097:blood* / Q000188:drug therapy* / Q000209:etiology; D004305:Dose-Response Relationship, Drug; D004921:Erythropoietin / Q000008:administration & dosage*; D006454:Hemoglobins / Q000378:metabolism; D006801:Humans; D007676:Kidney Failure, Chronic / Q000150:complications; D016014:Linear Models; D066230:Patient-Specific Modeling / Q000706:statistics & numerical data; D011994:Recombinant Proteins / Q000008:administration & dosage
chemical_list: C554270:EPO protein, human; D006454:Hemoglobins; D011994:Recombinant Proteins; D004921:Erythropoietin
keywords: Anemia management; Erythropoietin; Hemoglobin; Personalized medicine; Robust identification