Abstract
Sepsis is a severe medical condition caused by an inordinate immune response to an infection. Early detection of sepsis symptoms is important to prevent the progression into the more severe stages of the disease, which kills one in four it effects. Electronic medical records of 1492 patients containing 233 cases of sepsis were used in a clustering analysis to identify features that are indicative of sepsis and can be further used for training a Bayesian inference network. The Bayesian network was constructed using the systemic inflammatory response syndrome criteria, mean arterial pressure, and lactate levels for sepsis patients. The resulting network reveals a clear correlation between lactate levels and sepsis. Furthermore, it was shown that lactate levels may be predicative of the SIRS criteria. In this light, Bayesian networks of sepsis patients hold the promise of providing a clinical decision support system in the future.
Original language | English (US) |
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Title of host publication | 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012 |
DOIs | |
State | Published - 2012 |
Event | 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012 - Las Vegas, NV, United States Duration: Feb 23 2012 → Feb 25 2012 |
Other
Other | 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012 |
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Country | United States |
City | Las Vegas, NV |
Period | 2/23/12 → 2/25/12 |
Keywords
- Bayesian Network
- CDSS
- Clustering
- Decisison support system
- EMR
- Sepsis
- Septic shock
- Severe Sepsis
ASJC Scopus subject areas
- Biomedical Engineering
- Applied Mathematics