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Deep Learning/Neural Network

중환자실의 모니터링 장비의 정보를 ANN으로 디자인해서, 48시간이내의 악화를 탐지



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중요도(임상적파급력, 유용성) :2.5
회기적신기술사용수준 : 2

 

 

날짜 : 2016-08
저널 : IEEE 공학논문
국가 : 미국
대학 또는 연구소 :  필라델피아 어린이병원, 소아과, 마취과
저자:

 

 

한줄요약 : 중환자실에 처음들어오면 첫48시간동안 여러 모니터링 장비들을 통해 수집한 각 기관의 정보를 통해, 데이터를 카테고라이즈하고, 피쳐들을 랭킹한뒤, ANN 에 넣어서, 임상적으로 악화되는 지를 탐지한다. (CDSS classifier, SOFA score, SAPS-III score).

 


 

 

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2520-2524. doi: 10.1109/EMBC.2016.7591243.

Advanced analytics for outcome prediction in intensive care units.

Abstract

In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome. Then, we design an artificial neural network to serve as a classifier to detect patients at high risk of critical deterioration. We use the eight-fold cross validation method to test the developed CDSS classifier. The results from the classification show that the newly designed CDSS outperforms the widely used acuity scoring systems, SOFA and SAPS-III. The F-score classification result of our developed algorithms is 42% while the F-score results for SOFA and SAPS-III are 26% and 29% respectively.

 

PMID: 28268836 [Indexed for MEDLINE]
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