Temporal Graphical Modeling for Understanding and Preventing Autonomic Dysreflexia

Graphical modeling (GM) refers to a statistical modeling framework that uses probabilistic graphs to encode conditional dependencies and independences among variables of interest [1]. Due to its ability to represent complex probability distributions, GM has been developed and adapted to various fields including earth science [2], biology [3], health [4], and medicine [5] to dissect the structure of targeted phenomena to improve the understanding of the mechanism and decision-making process. Multivariate Time series (MTS), characterized by sequential observations of several variables over time, is pervasive in various disciplines. Understanding variables' temporal evolution and spatial dependencies is essential for uncovering patterns, trends, and anomalies that unfold over time. Time series analysis provides insights into the underlying dynamics of phenomena, enabling researchers and practitioners to make informed predictions, learn temporal dependencies, and formulate effective strategies. The exploration of temporal graphical models (TGMs) in the realm of time series analysis is paramount, given the crucial role that time series data plays in various fields. In this context, TGMs emerge as a sophisticated approach to modeling time series data, capturing the dynamic and evolving relationships among variables. As a validation scenario, we are working with the Swiss Paraplegic Centre (SPZ) to improve the long-term prognosis of individuals with spinal cord injury (SCI). Secondary health conditions (SHC) such as pulmonary infections, pressure injuries (PI), AD, and cardiovascular dysregulation are highly prevalent among the SCI population, imposing a tremendous risk on the patient’s health and the healthcare system [6,7]. Through the medical records and multimodal-sensory monitoring system, we expect to develop an intelligent system that can fuse different data sources, interact with clinical operation and expertise, and guide the medical treatment and decision-making for SHC management of SCI individuals. This project will focus on the TGM of a multivariate time series extracted from a wearable multi-modal sensing system. We aim to learn the high-resolution temporal dynamics together with spatial dependencies among biometrics, based on which the early detection and inference of AD episodes could be enabled. For more details about the task description, please refer to the attached document!

Graphical modeling (GM) refers to a statistical modeling framework that uses probabilistic graphs to encode conditional dependencies and independences among variables of interest [1]. Due to its ability to represent complex probability distributions, GM has been developed and adapted to various fields including earth science [2], biology [3], health [4], and medicine [5] to dissect the structure of targeted phenomena to improve the understanding of the mechanism and decision-making process.

Multivariate Time series (MTS), characterized by sequential observations of several variables over time, is pervasive in various disciplines. Understanding variables' temporal evolution and spatial dependencies is essential for uncovering patterns, trends, and anomalies that unfold over time. Time series analysis provides insights into the underlying dynamics of phenomena, enabling researchers and practitioners to make informed predictions, learn temporal dependencies, and formulate effective strategies. The exploration of temporal graphical models (TGMs) in the realm of time series analysis is paramount, given the crucial role that time series data plays in various fields. In this context, TGMs emerge as a sophisticated approach to modeling time series data, capturing the dynamic and evolving relationships among variables.

As a validation scenario, we are working with the Swiss Paraplegic Centre (SPZ) to improve the long-term prognosis of individuals with spinal cord injury (SCI). Secondary health conditions (SHC) such as pulmonary infections, pressure injuries (PI), AD, and cardiovascular dysregulation are highly prevalent among the SCI population, imposing a tremendous risk on the patient’s health and the healthcare system [6,7]. Through the medical records and multimodal-sensory monitoring system, we expect to develop an intelligent system that can fuse different data sources, interact with clinical operation and expertise, and guide the medical treatment and decision-making for SHC management of SCI individuals. This project will focus on the TGM of a multivariate time series extracted from a wearable multi-modal sensing system. We aim to learn the high-resolution temporal dynamics together with spatial dependencies among biometrics, based on which the early detection and inference of AD episodes could be enabled.

For more details about the task description, please refer to the attached document!

Up until now, there have been studies on identifying the risk factors for AD such as acute pain, or stimuli from the full bladder or bowel[8]. Yet research on detailed autonomic nervous interactions and quantitative relations among those relevant factors is lacking. The project dives into causal graphical modeling of MTS extracted from a wearable multi-modal sensing system. We aim to learn the fine-grained interactions and dynamics among biometrics, based on which an early warning signal can be constructed, and the effects of AD episodes can be reliably predicted among the SCI population. References 1. Glymour, C., Zhang, K. & Spirtes, P. Review of Causal Discovery Methods Based on Graphical Models. Front. Genet. 10, 524 (2019). 2. Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019). 3. Friedman, N. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303, 799–805 (2004). 4. Kalisch, M. et al. Understanding human functioning using graphical models. BMC Med. Res. Methodol. 10, 14 (2010). 5. Richens, J. G. Improving the accuracy of medical diagnosis with causal machine learning. 6. Haisma, J. et al. Complications following spinal cord injury: occurrence and risk factors in a longitudinal study during and after inpatient rehabilitation. J. Rehabil. Med. 39 5, 393–8 (2007). 7. Joseph, C. & Wikmar, L. N. Prevalence of secondary medical complications and risk factors for pressure ulcers after traumatic spinal cord injury during acute care in South Africa. Spinal Cord 54, 535–539 (2016). 8. Krassioukov, A., Warburton, D. E., Teasell, R. & Eng, J. J. A Systematic Review of the Management of Autonomic Dysreflexia After Spinal Cord Injury. Arch. Phys. Med. Rehabil. 90, 682–695 (2009).

Up until now, there have been studies on identifying the risk factors for AD such as acute pain, or stimuli from the full bladder or bowel[8]. Yet research on detailed autonomic nervous interactions and quantitative relations among those relevant factors is lacking. The project dives into causal graphical modeling of MTS extracted from a wearable multi-modal sensing system. We aim to learn the fine-grained interactions and dynamics among biometrics, based on which an early warning signal can be constructed, and the effects of AD episodes can be reliably predicted among the SCI population.

References
1. Glymour, C., Zhang, K. & Spirtes, P. Review of Causal Discovery Methods Based on Graphical Models. Front. Genet. 10, 524 (2019).
2. Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019).
3. Friedman, N. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303, 799–805 (2004).
4. Kalisch, M. et al. Understanding human functioning using graphical models. BMC Med. Res. Methodol. 10, 14 (2010).
5. Richens, J. G. Improving the accuracy of medical diagnosis with causal machine learning.
6. Haisma, J. et al. Complications following spinal cord injury: occurrence and risk factors in a longitudinal study during and after inpatient rehabilitation. J. Rehabil. Med. 39 5, 393–8 (2007).
7. Joseph, C. & Wikmar, L. N. Prevalence of secondary medical complications and risk factors for pressure ulcers after traumatic spinal cord injury during acute care in South Africa. Spinal Cord 54, 535–539 (2016).
8. Krassioukov, A., Warburton, D. E., Teasell, R. & Eng, J. J. A Systematic Review of the Management of Autonomic Dysreflexia After Spinal Cord Injury. Arch. Phys. Med. Rehabil. 90, 682–695 (2009).

Host: Dr. Diego Paez (SCAI-Lab, ETHZ | SPZ) Supervision: Dr. Diego Paez & Yanke Li (SCAI-Lab, ETHZ | SPZ) Please send your CV and the latest transcript of records to Yanke Li (yanke.li@hest.ethz.ch)

Host: Dr. Diego Paez (SCAI-Lab, ETHZ | SPZ)
Supervision: Dr. Diego Paez & Yanke Li (SCAI-Lab, ETHZ | SPZ)
Please send your CV and the latest transcript of records to Yanke Li (yanke.li@hest.ethz.ch)