scispace - formally typeset
N

Neil D. Evans

Researcher at University of Warwick

Publications -  92
Citations -  1726

Neil D. Evans is an academic researcher from University of Warwick. The author has contributed to research in topics: Identifiability & Nonlinear system. The author has an hindex of 22, co-authored 91 publications receiving 1555 citations.

Papers
More filters
Journal ArticleDOI

Efficient Removal of Immunoglobulin Free Light Chains by Hemodialysis for Multiple Myeloma: In Vitro and In Vivo Studies

TL;DR: Extended hemodialysis with the Gambro HCO 1100 dialyzer allowed continuous, safe removal of FLC in large amounts, and in vitro studies indicated that thisdialyzer was the most efficient of seven tested.
Journal ArticleDOI

Treatment of acute renal failure secondary to multiple myeloma with chemotherapy and extended high cut-off hemodialysis.

TL;DR: In dialysis-dependent acute renal failure secondary to myeloma kidney, patients who received uninterrupted chemotherapy and extended HCO-HD had sustained reductions in serum free light chain concentrations and recovered independent renal function.
Journal ArticleDOI

Identifiability of uncontrolled nonlinear rational systems

TL;DR: An approach for the identifiability analysis of uncontrolled rational systems is provided that, provided the model satisfies an observability rank condition, the state trajectories of an uncontrolled system corresponding to parameter vectors with outputs that are identical locally in time are connected via a smooth transformation.
Journal ArticleDOI

Extensions to a procedure for generating locally identifiable reparameterisations of unidentifiable systems.

TL;DR: In this article, extensions to an existing procedure for generating locally identifiable reparameterisations of unidentifiable systems are presented, which further formalise the constructive nature of the methodology and lend themselves to application within symbolic manipulation packages.
Journal ArticleDOI

The structural identifiability of the susceptible infected recovered model with seasonal forcing.

TL;DR: It is shown that the SIR epidemic model, with the force of infection subject to seasonal variation, and a proportion of either the prevalence or the incidence measured, is unidentifiable unless certain key system parameters are known, or measurable.