Abstract
Sepsis is a disease with a high mortality rate of 15%–50%. It is of great significance to study disease development rules of sepsis patients, which can summarize the clinical pattern and provide support for clinicians.
This paper proposes a complex network-based model of sepsis disease progression, which can quantify and study the transition law of sepsis patients. The paper presents that the human body is abstracted into a complex system composed of seven organ systems and the patient’s condition state at every moment is expressed as a seven-dimensional vector. The complex network of sepsis disease regression is constructed by using the disease states as nodes and the state changes as connecting edges. The transition law of sepsis patients’ illness states is that the complex network of sepsis is scale-free but does not have small-world characteristics. The important state nodes in the network determine the changes of patients’ condition, and patients will eventually leave Intensive Care Unit(ICU) or die in ICU. Clinicians should pay attention to intermediate state nodes, especially to patients’ respiratory system.
First Author and Second Author contribute equally to this work.
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KS test comparison of alternative hypothetical, (a) Maximum likelihood ratio R, (b) Significance level p
Comparison of alternative hypothetical fitting (a) degree, (b) in-degree, (c) out-degree
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Wang, R., Liu, J., Chen, Z., Gong, M., Li, C., Guo, W. (2022). The Transition Law of Sepsis Patients’ Illness States Based on Complex Network. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_31
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