Manifold ranking based scoring system with its application to cardiac arrest prediction: A retrospective study in emergency department patients.
BACKGROUND
The recently developed geometric distance scoring system has shown the effectiveness of scoring systems in predicting cardiac arrest within 72h and the potential to predict other clinical outcomes. However, the geometric distance scoring system predicts scores based on only local structure embedded by the data, thus leaving much room for improvement in terms of prediction accuracy.


METHODS
We developed a novel scoring system for predicting cardiac arrest within 72h. The scoring system was developed based on a semi-supervised learning algorithm, manifold ranking, which explores both the local and global consistency of the data. System evaluation was conducted on emergency department patients׳ data, including both vital signs and heart rate variability (HRV) parameters. Comparison of the proposed scoring system with previous work was given in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV).


RESULTS
Out of 1025 patients, 52 (5.1%) met the primary outcome. Experimental results show that the proposed scoring system was able to achieve higher area under the curve (AUC) on both the balanced dataset (0.907 vs. 0.824) and the imbalanced dataset (0.774 vs. 0.734) compared to the geometric distance scoring system.


CONCLUSIONS
The proposed scoring system improved the prediction accuracy by utilizing the global consistency of the training data. We foresee the potential of extending this scoring system, as well as manifold ranking algorithm, to other medical decision making problems. Furthermore, we will investigate the parameter selection process and other techniques to improve performance on the imbalanced dataset.
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: liut0012@e.ntu.edu.sg.
Tianchi
T
Liu
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: ezplin@ntu.edu.sg.
Zhiping
Z
Lin
Department of Emergency Medicine, Singapore General Hospital, Singapore; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore. Electronic address: marcus.ong.e.h@sgh.com.sg.
Marcus Eng Hock
ME
Ong
Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address: koh.zhi.xiong@sgh.com.sg.
Zhi Xiong
ZX
Koh
Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address: pek.pin.pin@sgh.com.sg.
Pin Pin
PP
Pek
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: ykyeo@ntu.edu.sg.
Yong Kiang
YK
Yeo
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: bsoh@ntu.edu.sg.
Beom-Seok
BS
Oh
SingHealth Emergency Medicine Residency Program, Singapore Health Services, Singapore. Electronic address: sophronesis@gmail.com.
Andrew Fu Wah
AF
Ho
Department of Emergency Medicine, Singapore General Hospital, Singapore; Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore. Electronic address: liu.nan@sgh.com.sg.
Nan
N
Liu
D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D004636:Emergency Service, Hospital; D005260:Female; D006323:Heart Arrest / Q000175:diagnosis*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D055986:Vital Signs
Cardiac arrest; Emergency medicine; Machine learning; Manifold ranking; Scoring system