A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. (February 2019)

• Record Type: Journal Article
• Authors: van Wyk, Franco; Khojandi, Anahita; Mohammed, Akram; Begoli, Edmon; Davis, Robert L.; Kamaleswaran, Rishikesan
• Is Part Of: International journal of medical informatics. Volume 122(2019)
• Page Start: 55
• Page End: 62
• Publication Date: 2019-02
• Subjects: Sepsis -- Physiological data -- Artificial intelligence -- Predictive model -- Critical care; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.12.002
• Languages: English
• ISSNs: 1386-5056

Abstract

Abstract: Purpose: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage. Methods: A data-set consisting of high-frequency physiological data from 1161 critically ill patients was analyzed. 377 patients had developed sepsis, and had data at least 3 h prior to the onset of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients in real-time using a total of 132 features extracted from a moving time-window. The model was trained on 80% of the patients and was tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 h prior to onset. Results: The model that used continuous physiological data alone resulted in sensitivity and F1 score of up to 80% and 67% one hour before sepsis onset. On average, these models were able to predict sepsis 294.19 ± 6.50 min (5 h) before the onset. Conclusions: The use of machine learning algorithms on continuous streams of physiological data can allow for early identification of at-risk patients in real-time with high accuracy.