10.6084/M9.FIGSHARE.11317196.V1
Jörn Schulz
Jörn
Schulz
Jan Terje Kvaløy
Jan Terje
Kvaløy
Kjersti Engan
Kjersti
Engan
Trygve Eftestøl
Trygve
Eftestøl
Samwel Jatosh
Samwel
Jatosh
Haydom Lutheran Hospital
Hussein Kidanto
Hussein
Kidanto
Aga Khan University
Hege Ersdal
Hege
Ersdal
University of Stavanger
Stavanger University Hospital
State transition modeling of complex monitored health data
<p>This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.</p>
Medicine
Biotechnology
69999 Biological Sciences not elsewhere classified
19999 Mathematical Sciences not elsewhere classified
Inorganic Chemistry
Computational Biology
Taylor & Francis
2019
2019-12-04
2022-10-09
Journal contribution
1679910 Bytes
10.1080/02664763.2019.1698523
10.6084/m9.figshare.11317196
CC BY 4.0