Learning similarities between irregularly sampled short multivariate time series from EHRs
Permanent lenke
https://hdl.handle.net/10037/10223Dato
2016-12-04Type
Conference objectKonferansebidrag
Forfatter
Mikalsen, Karl Øyvind; Bianchi, Filippo Maria; Soguero-Ruiz, Cristina; Skrøvseth, Stein Olav; Lindsetmo, Rolv-Ole; Revhaug, Arthur; Jenssen, RobertSammendrag
A large fraction of the Electronic Health Records
consists of clinical multivariate time series. Building models for
extracting information from these is important for improving
the understanding of diseases, patient care and treatment. Such
time series are oftentimes particularly challenging since they are
characterized by multiple, possibly dependent variables, length
variability and irregular samples. To deal with these issues when
such data are processed we propose a probabilistic approach
for learning pairwise similarities between the time series. These
similarities constitute a kernel matrix that can be used for many
different purposes. In this work it is used for clustering and
data characterization. We consider two different multivariate
time series datasets, one of them consisting of physiological
measurements from the Department of Gastrointestinal Surgery
at The University Hospital of North Norway and we show the
proposed method’s robustness and ability of dealing with missing
data. Finally we give a clinical interpretation of the clustering
results.
Beskrivelse
Presentation from the 3rd International Workshop on Pattern Recognition for Healthcare Analytics at ICPR 2016. Held in Cancun, 04.12.2016.