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

2018/03/21 by Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Mikalsen, Karl Øyvind +8 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · 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 #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1803.07879

arxiv created 2018/03/21 · openalex publication_date 2018/03/21 · arxiv updated 2018/03/22 · openalex created_date 2022/09/30 · openalex updated_date 2026/07/28

Abstract

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

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

Cited by

• Time series cluster kernels to exploit informative missingness and incomplete label information

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