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Big Data and ICU scoring systems

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Big Data and ICU scoring systems. / Todd, James; Richards, Brent; Vanstone, Bruce J et al.
2017.

Research output: Contribution to conference › Paper

HarvardHarvard

Todd, J, Richards, B, Vanstone, BJ & Gepp, A 2017, 'Big Data and ICU scoring systems'. <http://www.intellihq.com.au/july21st-2017-ai-research-day/>

APA

Todd, J., Richards, B., Vanstone, B. J., & Gepp, A. (2017). Big Data and ICU scoring systems. http://www.intellihq.com.au/july21st-2017-ai-research-day/

CBE

Todd J, Richards B, Vanstone BJ, Gepp A. 2017. Big Data and ICU scoring systems.

MLA

Todd, James et al. Big Data and ICU scoring systems. Paper, 2017.

VancouverVancouver

Todd J, Richards B, Vanstone BJ, Gepp A. Big Data and ICU scoring systems. 2017.

Author

Todd, James ; Richards, Brent ; Vanstone, Bruce J et al. / Big Data and ICU scoring systems.

RIS

TY - CONF

T1 - Big Data and ICU scoring systems

AU - Todd, James

AU - Richards, Brent

AU - Vanstone, Bruce J

AU - Gepp, Adrian

N1 - AI in Intensive Care Research Day ; Conference date: 21-07-2017

PY - 2017/7/21

Y1 - 2017/7/21

N2 - Severity scoring systems are used in intensive care units for stratifying patients in clinical research and benchmarking ICU performance. A variant of the Acute Physiology and Chronic Health Evaluation (APACHE) system is used in Australia for benchmarking purposes – the APACHE III-j. This and other major scoring systems have been developed under an old paradigm of minimum data collection, while the current paradigm is to use all useful data. The APACHE III-j system uses the worst observations in the first 24-hours of a patient’s ICU stay, ignoring the rest of the distributional information. We hypothesise that scoring system performance can be improved by adding variables that capture this ignored distributional information. To test this hypothesis, the APACHE III-j system will be replicated and compared to a modified version that adds metrics describing the distribution of an underlying physiology variable utilising high frequency data.

AB - Severity scoring systems are used in intensive care units for stratifying patients in clinical research and benchmarking ICU performance. A variant of the Acute Physiology and Chronic Health Evaluation (APACHE) system is used in Australia for benchmarking purposes – the APACHE III-j. This and other major scoring systems have been developed under an old paradigm of minimum data collection, while the current paradigm is to use all useful data. The APACHE III-j system uses the worst observations in the first 24-hours of a patient’s ICU stay, ignoring the rest of the distributional information. We hypothesise that scoring system performance can be improved by adding variables that capture this ignored distributional information. To test this hypothesis, the APACHE III-j system will be replicated and compared to a modified version that adds metrics describing the distribution of an underlying physiology variable utilising high frequency data.

M3 - Paper

ER -