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Soft Computing Methods for Drug Dosing in Renal Anemia

Soft Computing Methods for Drug Dosing in Renal Anemia. Adam E. Gaweda Kidney Disease Program University of Louisville Louisville, KY http://kdp.louisville.edu. Overview. Anemia in End Stage Renal Disease Soft Computing in Anemia Treatment Intelligent modeling and control for drug dosing

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Soft Computing Methods for Drug Dosing in Renal Anemia

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  1. Soft Computing Methods for Drug Dosing in Renal Anemia Adam E. Gaweda Kidney Disease Program University of Louisville Louisville, KY http://kdp.louisville.edu

  2. Overview • Anemia in End Stage Renal Disease • Soft Computing in Anemia Treatment • Intelligent modeling and control for drug dosing • Perception-based computing in clinical decision support • Summary

  3. Mechanism of Red Blood Cell Production Erythropoiesis Hypoxia RBC lifetime = about 120 days Erythropoietin

  4. Anemia Management in End Stage Renal Disease Erythropoiesis RBC lifetime = about 60 days hematocrit Vol. % of RBCin blood Erythropoietin

  5. Clinical Practice • Dialysis Outcome Quality Initiative (DOQI) guideline: • Maintain hematocrit between 33-36 Vol.% • Protocol driven • Anemia Management Protocol • Frequent dosing changes • Dose adjusted in 53% of patients / month • Mean dose adjustment 1390±2648 units

  6. Challenges • Target guidelines very narrow: • Only 30% of healthy population would meet range comparable to DOQI guideline ( ±1.5 Vol. % ) • Erythropoietin administration: • Non-physiological dosing: • Discrete and intermittent • Different routes of administration: SC vs. IV

  7. Soft Computing Methods in Anemia Treatment • Intelligent Modeling and Control • Computing with Perceptions

  8. Intelligent Modeling and Control • The existence of data records allows for incorporation of data-driven learning: • Neural Networks for dose-response modeling • Direct Inverse Neuro-Control for Erythropoietin administration

  9. Control Theoretic Approach to Anemia Treatment Hematocrit level as specified by Dialysis Outcomes Quality Initiative ( 33 - 36 Vol. % ) PATIENT / PLANT PHYSICIAN / DECISION MAKER Erythropoietin Hematocrit

  10. HCT(k) b1 b2 tanh x  EPO(k-1) z-1  HCT(k+1) EPO(k) Patient Model HCT HCT EPO

  11. Neuro-Controller HCT HCT(k-2) HCT(k-1) HCT(k) EPO(k) z-1 b1 b2 EPO tanh x z-1 EPO EPO(k+1)

  12. Development of Plant Model and Controller Treatment Data (1996 – 2000) Levenberg-Marquardt Levenberg-Marquardt PLANT CONTROLLER z-1 tanh x  z-1 tanh x z-1

  13. Direct Inverse Neuro-Control Noise S HCT CONTROLLER PLANT z-1 tanh x  z-1 tanh x z-1 EPO

  14. Simulation Results – Anemia Management Protocol HCT months EPO

  15. Simulation Results –Neuro-Control HCT months EPO

  16. Simulation Results – Neuro-Control vs. Protocol

  17. Computing with Perceptions • Imprecision exists due to the complexity of human body as well as the quality of data, i.e. laboratory data • Perception-based Fuzzy System • Prediction of approximate response to Erythropoietin treatment

  18. Perception-based Fuzzy System Rj: IF x is Aj THEN y is Bj FUZZYFICATION INFERENCE DEFUZZYFICATION y x

  19. Computing with Perceptions – Perception as Fuzzy Number Asymmetric Gaussian Membership Function  (x) sr sl m x

  20. Computing with Perceptions – Imprecise Input IF x is A THEN y is B A x = A’ x

  21. Computing with Perceptions –Mutual Subsethood A A B B A  B A  B

  22. Computing with Perceptions –Imprecise Output

  23. Perception-based Fuzzy System Example from 166 subjects R1 If HCT is LO and EPO is ME then  HCT is PO R2 If HCT is LO and EPO is LO then  HCT is ZE R3 If HCT is HI and EPO is LO then  HCT is NE R4 If HCT is LO and EPO is HI then  HCT is PO LO HI NE ZE PO LO ME HI

  24. Perception-based Fuzzy System Rule Evaluation

  25. A Look Into The (Near) Future • By developing this approach further, we will be able to incorporate “non-measurable quantities” such as: • Quality of Life • Morbidity into an intelligent clinical decision support system for treatment of chronic illnesses.

  26. Summary • Intelligent Systems and their ability to: • Learn from data • Deal with imprecision have been demonstrated to be a valuable asset for improving the clinical practice in treatment of chronic illnesses.

  27. Thank you

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