An Unsupervised Multivariate Time Series Kernel Approach for Identifying\n Patients with Surgical Site Infection from Blood Samples

2018/03/21 by Karl Øyvind Mikalsen, Mikalsen, Karl Øyvind, Cristina Soguero-Ruíz +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Bacterial Identification and Susceptibility Testing #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Time Series Analysis and Forecasting #Traditional Chinese Medicine Studies

paper · pdf · doi:10.48550/arxiv.1803.07879

openalex publication_date 2018/03/21 · openalex created_date 2022/09/30 · openalex updated_date 2026/07/28

Abstract

A large fraction of the electronic health records consists of clinical\nmeasurements collected over time, such as blood tests, which provide important\ninformation about the health status of a patient. These sequences of clinical\nmeasurements are naturally represented as time series, characterized by\nmultiple variables and the presence of missing data, which complicate analysis.\nIn this work, we propose a surgical site infection detection framework for\npatients undergoing colorectal cancer surgery that is completely unsupervised,\nhence alleviating the problem of getting access to labelled training data. The\nframework is based on powerful kernels for multivariate time series that\naccount for missing data when computing similarities. Our approach show\nsuperior performance compared to baselines that have to resort to imputation\ntechniques and performs comparable to a supervised classification baseline.\n

Citations

• Inference and missing data
• Multiple imputation using chained equations: Issues and guidance for practice
• The Impact of Surgical-Site Infections Following Orthopedic Surgery at a Community Hospital and a University Hospital Adverse Quality of Life, Excess Length of Stay, and Extra Cost
• Review: A gentle introduction to imputation of missing values
• Recurrent Neural Networks for Multivariate Time Series with Missing Values

Related

• Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
• Learning compressed representations of blood samples time series with missing data
• Prognostics of Surgical Site Infections using Dynamic Health Data
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• On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit