Limits...
Cumulative Time Series Representation for Code Blue prediction in the Intensive Care Unit.

Salas-Boni R, Bai Y, Hu X - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: The health status of patients is represented both by a term frequency approach, TF, often used in natural language processing; and by our novel cumulative approach.We call this representation "weighted accumulated occurrence representation", or WAOR.We obtained a better performance with our cumulative representation, retaining a sensitivity close to our previous work while improving the other metrics.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiological Nursing, University of California, San Francisco, CA.

ABSTRACT
Patient monitors in hospitals generate a high number of false alarms that compromise patients care and burden clinicians. In our previous work, an attempt to alleviate this problem by finding combinations of monitor alarms and laboratory test that were predictive of code blue events, called SuperAlarms. Our current work consists of developing a novel time series representation that accounts for both cumulative effects and temporality was developed, and it is applied to code blue prediction in the intensive care unit (ICU). The health status of patients is represented both by a term frequency approach, TF, often used in natural language processing; and by our novel cumulative approach. We call this representation "weighted accumulated occurrence representation", or WAOR. These two representations are fed into a L1 regularized logistic regression classifier, and are used to predict code blue events. Our performance was assessed online in an independent set. We report the sensitivity of our algorithm at different time windows prior to the code blue event, as well as the work-up to detect ratio and the proportion of false code blue detections divided by the number of false monitor alarms. We obtained a better performance with our cumulative representation, retaining a sensitivity close to our previous work while improving the other metrics.

No MeSH data available.


Related in: MedlinePlus

A comparison of code blue cases and control patients. In this image, each column represents one sample p(t), and the i-th row the i-th value of the vector p(t), that is, how represented the i-th SuperAlarm is at the time t, using the representation WAORabs.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4525278&req=5

f1-2073367: A comparison of code blue cases and control patients. In this image, each column represents one sample p(t), and the i-th row the i-th value of the vector p(t), that is, how represented the i-th SuperAlarm is at the time t, using the representation WAORabs.


Cumulative Time Series Representation for Code Blue prediction in the Intensive Care Unit.

Salas-Boni R, Bai Y, Hu X - AMIA Jt Summits Transl Sci Proc (2015)

A comparison of code blue cases and control patients. In this image, each column represents one sample p(t), and the i-th row the i-th value of the vector p(t), that is, how represented the i-th SuperAlarm is at the time t, using the representation WAORabs.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525278&req=5

f1-2073367: A comparison of code blue cases and control patients. In this image, each column represents one sample p(t), and the i-th row the i-th value of the vector p(t), that is, how represented the i-th SuperAlarm is at the time t, using the representation WAORabs.
Bottom Line: The health status of patients is represented both by a term frequency approach, TF, often used in natural language processing; and by our novel cumulative approach.We call this representation "weighted accumulated occurrence representation", or WAOR.We obtained a better performance with our cumulative representation, retaining a sensitivity close to our previous work while improving the other metrics.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiological Nursing, University of California, San Francisco, CA.

ABSTRACT
Patient monitors in hospitals generate a high number of false alarms that compromise patients care and burden clinicians. In our previous work, an attempt to alleviate this problem by finding combinations of monitor alarms and laboratory test that were predictive of code blue events, called SuperAlarms. Our current work consists of developing a novel time series representation that accounts for both cumulative effects and temporality was developed, and it is applied to code blue prediction in the intensive care unit (ICU). The health status of patients is represented both by a term frequency approach, TF, often used in natural language processing; and by our novel cumulative approach. We call this representation "weighted accumulated occurrence representation", or WAOR. These two representations are fed into a L1 regularized logistic regression classifier, and are used to predict code blue events. Our performance was assessed online in an independent set. We report the sensitivity of our algorithm at different time windows prior to the code blue event, as well as the work-up to detect ratio and the proportion of false code blue detections divided by the number of false monitor alarms. We obtained a better performance with our cumulative representation, retaining a sensitivity close to our previous work while improving the other metrics.

No MeSH data available.


Related in: MedlinePlus