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A Probabilistic Broken-stick Model for CKD Staging and Risk Stratification

Poh, Norman, Bull, S, Tirunagari, Santosh and de Lusignan, Simon (2017) A Probabilistic Broken-stick Model for CKD Staging and Risk Stratification In: BRS 2017 Conference, 2017-04-26 - 2017-04-28, Nottingham, UK.

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Abstract

Introduction: Determining CKD stage and disease progression based on eGFR in primary care is complicated by the fact that the measurements are irregularly sampled and influenced by both genuine physiological changes and external factors. Models used for these purposes would ideally capture both short- (for staging) and long-term (for progression) trends. However, existing regression algorithms such as linear, polynomial and Gaussian process regression either cannot account for these challenges or do not satisfy the key clinical requirements of providing an easily interpretable model that can elucidate short- and long-term trends. In order to balance interpretability and flexibility, an extension to broken-stick regression models is proposed in order to make them more suitable for modelling clinical time series.

Item Type: Conference or Workshop Item (Conference Poster)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Poh, NormanN.Poh@surrey.ac.ukUNSPECIFIED
Bull, SUNSPECIFIEDUNSPECIFIED
Tirunagari, Santoshsantosh.tirunagari@surrey.ac.ukUNSPECIFIED
de Lusignan, SimonS.Lusignan@surrey.ac.ukUNSPECIFIED
Date : 2017
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 10 Mar 2017 15:14
Last Modified : 19 Jul 2017 12:37
URI: http://epubs.surrey.ac.uk/id/eprint/813745

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