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A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data

100%

Nesaragi N. , Patidar S. , Thangaraj V.

2021

tom Vol. 41, no. 3

1013--1024

Early detection of sepsis can assist in clinical triage and decision-making, resulting in early intervention with improved outcomes. This study aims to develop a machine learning framework to predict the onset of sepsis through EHR data by applying tensor decomposition on correlation matrices of clinical covariates for every record, arranged on an hourly basis for the length of stay (LOS) in intensive care unit. A third-order tensor [...] representing a clinical correlation among selected 24 covariates for a considered time frame of sepsis onset duration of 6 h, with a stride of 1 h is formed for each record. Such a fused tensor with dimensions [...] for every record undergoes Tucker decomposition with an optimal choice of rank. The factor matrices U1; U2; U3 thus obtained after decomposition are excluded and only the core tensor r with a dimension [...] has been retained, and used to provide latent features for prediction of sepsis onset. A five-fold cross-validation scheme is employed wherein the obtained 100 latent features from the reshaped core tensor, are fed to Light Gradient Boosting Machine Learning models (LightGBM) for binary classification, further alleviating the involved class imbalance. The machine-learning framework is designed via Bayesian optimization, yielding an average normalized utility score of 0.4314 on publicly available PhysioNet/Computing in Cardiology Challenge 2019 training data. The proposed tensor decomposition deciphers the higher-order interrelations among the considered clinical covariates for early prediction of sepsis and the results obtained are on par with existing state-of-the-art performances.

Non-invasive waveform analysis for emergency triage via simulated hemorrhage: An experimental study using novel dynamic lower body negative pressure model

75%

Nesaragi N. , Høiseth L. Ø. , Qadir H. A. , Rosseland L. A. , Halvorsen P. S. , Balasingham I.

tom Vol. 43, no. 3

551--567

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time–frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, F1-score:72.16%, Sensitivity:79.06%, and Specificity:89.21%. Our proposed DL algorithm on PPG signal demonstrates the possibility to capture the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup.