Poster, Puentes Latinx Research Week, Ann Arbor, Michigan
Background: The quick sequential organ failure assessment (qSOFA) scoring system, defined by Sepsis-3 in 2016, is a method to identify patients at risk to progress to poor outcomes related to sepsis using variables that can be collected at the bedside.
Seminar, BISTRO, Ann Arbor, Michigan
Seminar, University of Michigan Tools and Technology Seminar, Ann Arbor, Michigan
Seminar, BISTRO, Ann Arbor, Michigan
Talk, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, Michigan
Risk stratification for sepsis-induced organ dysfunction is difficult because of its heterogeneity. Moreover, the information needed to compute the Sequential Organ Failure Assessment (SOFA) defined by Sepsis-3 may not be immediately available for patients that require care. Continuous electrocardiogram (ECG) recording is often available for patients in intensive care, and may be useful to identify those at risk of decompensating to septic shock. Additionally, structuring multi-lead ECG data as a higher-order tensor can preserve the data’s structure and prevent loss of information that can occur in traditional machine learning, which structures data as 1-dimensional vectors. The project discussed here is a first step in this research: the development of a tensorial data classification method, and its testing with publicly available ECG data. Methods: We developed a tensor classification method based on multilinear discriminant analysis via Kempf-Ness Theory (KNMDA). We compare performance of this method to existing tensor methods and traditional machine learning, including support vector machine (SVM) and linear discriminant analysis (LDA), at different noise levels. We constructed 3rd order tensors using 12-lead ECG data from the Physionet PTB database, which contains samples of both normal control and unhealthy subjects. Results: Over thirty iterations, our KNMDA method obtained average area under the receiver operating characteristic curve (AUC) of 0.94 (SD 0.03), an improvement compared to linear SVM with 0.91 (0.03) and LDA with 0.68 (0.07) with no additional noise. When signal-to-noise ratio of the ECG signal was changed to -10, KNMDA achieved average AUC of 0.92 (0.03), compared to SVM with 0.85 (0.06) and LDA with 0.59 (0.07). Impact: The results show that our proposed method can outperform existing ones when using real ECG data. We can therefore apply this method to our larger goal of improving risk-stratification for septic patients, to augment physician decision-making and improve patient care.
Seminar, BISTRO, Ann Arbor, Michigan
Signal processing methods can be used to obtain information from noninvasive monitoring, such as electrocardiogram (ECG) and photoplethysmogram (PPG). The information gained can be used as input for machine learning methods, which can provide computer-assisted support to clinicians. This presentation discusses applications of signal processing, such as using a wearable device to assess sleep quality, and using multi-lead ECG to aid in risk stratification for sepsis-induced organ dysfunction.
Talk, BIBM 2018, Madrid, Spain
Talk, Undergraduate Research Opportunity Program (UROP) Spring Research Symposium, Ann Arbor, Michigan